HomeAmazonAWS Certified AssociateAWS MLA-C01 Dumps (V8.02) – Simplify Your Path to AWS Certified Machine Learning Engineer – Associate Exam Success by Providing the Latest Materials

February 13, 2025

AWS MLA-C01 Dumps (V8.02) – Simplify Your Path to AWS Certified Machine Learning Engineer – Associate Exam Success by Providing the Latest Materials

Choose to earn the AWS Certified Machine Learning Engineer – Associate certification to validate your technical ability in implementing ML workloads in production and operationalizing them. It will boost your career profile and credibility, and position you for in-demand machine learning job roles. How to pass the MLA-C01 exam and earn the certification successfully? Come to DumpsBase to choose the latest AWS MLA-C01 dumps. The current version of the MLA-C01 dumps is V8.02, containing 125 practice exam questions and answers. It is the right study tool to help you achieve your certification goals. Furthermore, these MLA-C01 exam dumps are designed to break down complex topics into digestible sections, allowing you to absorb information effectively without feeling overloaded. DumpsBase’s step-by-step approach ensures that you gain a deep understanding of the MLA-C01 dumps (V8.02), covering every key concept required for the AWS Certified Machine Learning Engineer – Associate exam. Trust us, DumpsBase is here to simplify your path to success by providing the latest MLA-C01 dumps.

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1. You are a machine learning engineer at a fintech company tasked with developing and deploying an end-to-end machine learning workflow for fraud detection. The workflow involves multiple steps, including data extraction, preprocessing, feature engineering, model training, hyperparameter tuning, and deployment. The company requires the solution to be scalable, support complex dependencies between tasks, and provide robust monitoring and versioning capabilities. Additionally, the workflow needs to integrate seamlessly with existing AWS services.

Which deployment orchestrator is the MOST SUITABLE for managing and automating your ML workflow?

Use AWS Step Functions to build a serverless workflow that integrates with SageMaker for model training and deployment, ensuring scalability and fault tolerance

Use AWS Lambda functions to manually trigger each step of the ML workflow, enabling flexible execution without needing a predefined orchestration tool

Use Amazon SageMaker Pipelines to orchestrate the entire ML workflow, leveraging its built-in integration with SageMaker features like training, tuning, and deployment

Use Apache Airflow to define and manage the workflow with custom DAGs (Directed Acyclic Graphs), integrating with AWS services through operators and hooks

2. You are tasked with building a predictive model for customer lifetime value (CLV) using Amazon SageMaker. Given the complexity of the model, it’s crucial to optimize hyperparameters to achieve the best possible performance. You decide to use SageMaker’s automatic model tuning (hyperparameter optimization) with Random Search strategy to fine-tune the model. You have a large dataset, and the tuning job involves several hyperparameters, including the learning rate, batch size, and dropout rate. During the tuning process, you observe that some of the trials are not converging effectively, and the results are not as expected. You suspect that the hyperparameter ranges or the strategy you are using may need adjustment.

Which of the following approaches is MOST LIKELY to improve the effectiveness of the hyperparameter tuning process?

Decrease the number of total trials but increase the number of parallel jobs to speed up the tuning process

Switch from the Random Search strategy to the Bayesian Optimization strategy and narrow the range of critical hyperparameters

Use the Grid Search strategy with a wide range for all hyperparameters and increase the number of total trials

Increase the number of hyperparameters being tuned and widen the range for all hyperparameters

3. A company stores its training datasets on Amazon S3 in the form of tabular data running into millions of rows. The company needs to prepare this data for Machine Learning jobs. The data preparation involves data selection, cleansing, exploration, and visualization using a single visual interface.

Which Amazon SageMaker service is the best fit for this requirement?

Amazon SageMaker Feature Store

Amazon SageMaker Data Wrangler

SageMaker Model Dashboard

Amazon SageMaker Clarify

4. Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?

Relying solely on data encryption to protect the AI training data

Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities

Using a single authentication mechanism for all users and services accessing the AI models

Implementing a single-layer firewall to block unauthorized access to the AI models

5. You are an ML engineer at an e-commerce company tasked with building an automated recommendation system that scales during peak shopping seasons. The solution requires provisioning multiple compute resources, including SageMaker for model training, EC2 instances for data preprocessing, and an RDS database for storing user interaction data. You need to automate the deployment and management of these resources, ensuring that the stacks can communicate effectively. The company prioritizes infrastructure as code (IaC) to maintain consistency and scalability across environments.

Which approach is the MOST SUITABLE for automating the provisioning of compute resources and ensuring seamless communication between stacks?

Manually provision the SageMaker, EC2, and RDS resources using the AWS Management Console, ensuring that communication is established by manually updating security groups and networking configurations

Use AWS Elastic Beanstalk to deploy the entire ML solution, relying on its built-in environment management to handle the provisioning and communication between resources automatically

Use AWS CDK (Cloud Development Kit) to define the infrastructure in a high-level programming language, deploying each service as an independent stack without configuring inter-stack communication

Use AWS CloudFormation with nested stacks to automate the provisioning of SageMaker, EC2, and RDS resources, and configure outputs from one stack as inputs to another to enable communication between them

6. You are a data scientist at a healthcare startup tasked with developing a machine learning model to predict the likelihood of patients developing a specific chronic disease within the next five years. The dataset available includes patient demographics, medical history, lab results, and lifestyle factors, but it is relatively small, with only 1,000 records. Additionally, the dataset has missing values in some critical features, and the class distribution is highly imbalanced, with only 5% of patients labeled as having developed the disease.

Given the data limitations and the complexity of the problem, which of the following approaches is the MOST LIKELY to determine the feasibility of an ML solution and guide your next steps?

Proceed with training a deep neural network (DNN) model using the available data, as DNNs can handle small datasets by learning complex patterns

Increase the dataset size by generating synthetic data and then train a simple logistic regression model to avoid overfitting

Conduct exploratory data analysis (EDA) to understand the data distribution, address missing values, and assess the class imbalance before determining if an ML solution is feasible

Immediately apply an oversampling technique to balance the dataset, then train an XGBoost model to maximize performance on the minority class

7. You are a lead machine learning engineer at a growing tech startup that is developing a recommendation system for a mobile app. The recommendation engine must be able to scale quickly as the user base grows, remain cost-effective to align with the startup’s budget constraints, and be easy to maintain by a small team of engineers. The company has decided to use AWS for the ML infrastructure. Your goal is to design an infrastructure that meets these needs, ensuring that it can handle rapid scaling, remains within budget, and is simple to update and monitor.

Which combination of practices and AWS services is MOST LIKELY to result in a maintainable, scalable, and cost-effective ML infrastructure?

Implement Amazon SageMaker for model training, deploy the models using Amazon EC2 with manual scaling to handle inference, and use AWS CloudFormation for managing infrastructure as code to ensure repeatability

Use Amazon SageMaker for both training and deployment, leverage auto-scaling endpoints for real-time inference, and apply SageMaker Pipelines for orchestrating end-to-end ML workflows, ensuring scalability and automation

Use Amazon SageMaker for training, deploy models on Amazon ECS for flexible scaling, and implement infrastructure monitoring with a combination of CloudWatch and AWS Systems Manager to ensure maintainability

Train models using Amazon EMR for cost efficiency, deploy the models using AWS Lambda for serverless inference, and manually monitor the system using CloudWatch to reduce operational overhead

8. You are working on a machine learning project for a financial services company, developing a model to predict credit risk. After deploying the initial version of the model using Amazon SageMaker, you find that its performance, measured by the AUC (Area Under the Curve), is not meeting the company’s accuracy

requirements. Your team has gathered more data and believes that the model can be further optimized. You are considering various methods to improve the model’s performance, including feature engineering, hyperparameter tuning, and trying different algorithms. However, given the limited time and computational resources, you need to prioritize the most impactful strategies.

Which of the following approaches are the MOST LIKELY to lead to a significant improvement in model performance? (Select two)

Increase the size of the training dataset by incorporating synthetic data and then retrain the existing model

Perform hyperparameter tuning using Bayesian optimization and increase the number of trials to explore a broader search space

Switch to a more complex algorithm, such as deep learning, and use transfer learning to leverage pre-trained models

Use Amazon SageMaker Debugger to debug and improve model performance by addressing underlying problems such as overfitting, saturated activation functions, and vanishing gradients

Focus on feature engineering by creating

9. You are a data scientist at a healthcare company developing a machine learning model to analyze medical imaging data, such as X-rays and MRIs, for disease detection. The dataset consists of 10 million high-resolution images stored in Amazon S3, amounting to several terabytes of data. The training process requires processing these images efficiently to avoid delays due to I/O bottlenecks, and you must ensure that the chosen data access method aligns with the large dataset size and the high throughput requirements of the model.

Given the size and nature of the dataset, which SageMaker input mode and AWS Cloud Storage configuration is the MOST SUITABLE for this use case?

Select the Pipe input mode to stream the data directly from Amazon S3 to the training instances, allowing the model to start processing data immediately without requiring local storage for the entire dataset

Use the File input mode with EFS (Amazon Elastic File System) to mount the dataset across multiple instances, ensuring data is shared and accessible during distributed training

Implement the FastFile input mode with FSx for Lustre, to enable on-demand streaming of data chunks from Amazon S3 with low latency and high throughput

Use the File input mode to download the entire dataset from Amazon S3 to the training instances' local storage before starting the training process, ensuring that all data is available locally during training

10. You are working as a machine learning engineer for a startup that provides image recognition services. The service is currently in its beta phase, and the company expects varying levels of traffic, with some days having very few requests and other days experiencing sudden spikes. The company wants to minimize costs during low-traffic periods while still being able to handle large, infrequent spikes of requests efficiently. Given these requirements, you are considering using Amazon SageMaker for your deployment.

Which of the following statements is the BEST recommendation for the given scenario?

Use Amazon SageMaker Asynchronous Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently

Use Batch transform to run inference with Amazon SageMaker that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently

Use Amazon SageMaker Serverless Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently

Use Amazon SageMaker Real-time Inference that minimizes costs during low-traffic periods while managing large infrequent spikes of requests efficiently

11. You are an ML Engineer working for a logistics company that uses multiple machine learning models to optimize delivery routes in real-time. Each model needs to process data quickly to provide up-to-the-minute route adjustments, but the company also has strict cost constraints. You need to deploy the models in an environment where performance, cost, and latency are carefully balanced. There may be slight variations in the access frequency of the models. Any excessive costs could impact the project’s

profitability.

Which of the following strategies should you consider to balance the tradeoffs between performance, cost, and latency when deploying your model in Amazon SageMaker? (Select two)

Choose a lower-cost CPU instance, accepting longer inference times, as the savings on compute costs are more important than minimizing latency

Leverage Amazon SageMaker Neo to compile the model for optimized deployment on edge devices, reducing latency and cost but with limited scalability for large datasets

Use Amazon SageMaker’s multi-model endpoint to deploy multiple models on a single instance, reducing costs by sharing resources

Implement auto-scaling on a fleet of medium-sized instances, allowing the system to adjust resources based on real-time demand, balancing cost and performance dynamically

Deploy the model on a high-performance GPU instance to minimize latency, regardless of the higher cost, ensuring real-time route adjustments

12. You are a machine learning engineer at an e-commerce company that uses a recommendation model to suggest products to customers. The model was trained on data from the past year, but after being in production for several months, you notice that the model's recommendations are becoming less relevant. You suspect that either data drift or model drift could be causing the decline in performance. To investigate and resolve the issue, you need to understand the difference between these two types of drift and how to monitor them using Amazon SageMaker.

Which of the following statements BEST describes the difference between data drift and model drift, and how you would address them using Amazon SageMaker?

Data drift occurs when the distribution of the input data changes over time, while model drift happens when the model’s underlying assumptions or parameters become outdated. To address data drift, you should use SageMaker Model Monitor to track changes in input data distribution. For model drift, you should periodically retrain the model using the latest data

Data drift is a sudden change in the model’s accuracy, while model drift is a gradual degradation in model performance. You should use SageMaker Feature Store to manage both types of drift by standardizing input data

Data drift occurs when the model’s predictions start to deviate from the expected outcomes, while model drift occurs when the model's accuracy declines due to changes in the input data. SageMaker Pipelines should be used to automate retraining for both types of drift

Data drift refers to changes in the model’s accuracy due to shifts in the data, while model drift refers to changes in the underlying data features over time. To address both, you should use SageMaker Clarify to detect bias and retrain the model monthly

13. You are a data scientist at a pharmaceutical company that builds predictive models to analyze clinical trial data. Due to regulatory requirements, the company must maintain strict version control of all models used in decision-making processes. This includes tracking which data, hyperparameters, and code were

used to train each model, as well as ensuring that models can be easily reproduced and audited in the future. You decide to implement a system to manage model versions and track their lifecycle effectively.

Which of the following strategies is the MOST LIKELY to ensure model versioning, repeatability, and auditability?

Leverage the SageMaker Model Registry to register, track, and manage different versions of models, capturing all relevant metadata, including data sources, hyperparameters, and training code

Create a version control system in Git for the model’s training code and configuration files, while storing the trained models in a separate S3 bucket for easy retrieval

Use Amazon S3 to store each version of the model manually, tagging the stored files with metadata about the training data, hyperparameters, and code used for training

Use SageMaker Model Monitor to track the performance of models in production, ensuring that any changes in model behavior are documented for future audits

14. You are a machine learning engineer at a financial services company tasked with building a real-time fraud detection system. The model needs to be highly accurate to minimize false positives and false negatives. However, the company has a limited budget for cloud resources, and the model needs to be retrained frequently to adapt to new fraud patterns. You must carefully balance model performance, training time, and cost to meet these requirements.

Which of the following strategies is the MOST LIKELY to achieve an optimal balance between model performance, training time, and cost?

Deploy a simpler model like logistic regression to reduce training time and cost, while accepting a slight reduction in model accuracy

Implement a tree-based model like XGBoost with early stopping and hyperparameter tuning, balancing accuracy with reduced training time and computational cost

Use a deep neural network with multiple layers and complex architecture to maximize performance, even if it requires significant computational resources and longer training times

Choose a support vector machine (SVM) with a nonlinear kernel to enhance accuracy, regardless of the increased training time and cost associated with large datasets

15. You are an ML Engineer working for a healthcare company that uses a machine learning model to recommend personalized treatment plans to patients. The model is deployed on Amazon SageMaker and is critical to the company's operations, as any incorrect predictions could have significant consequences. A new version of the model has been developed, and you need to deploy it in production. However, you want to ensure that the deployment process is robust, allowing you to quickly roll back to the previous version if any issues arise. Additionally, you need to maintain version control for future updates and manage traffic between different model versions.

Which of the following strategies should you implement to ensure a smooth and reliable deployment of the new model version using Amazon SageMaker, considering best practices for versioning and rollback strategies? (Select two)

Utilize Amazon SageMaker’s blue/green deployment strategy to shift traffic gradually from the old model to the new one, ensuring that you can monitor performance and quickly revert if needed

Deploy the new model version immediately and redirect 100% of traffic to it, assuming it has been thoroughly tested and will not require a rollback

Use Amazon SageMaker’s built-in versioning to manage different versions of the model, and deploy the new version in a canary release by redirecting a small percentage of traffic to it initially

Create a backup of the current model, deploy the new version, and if any issues arise, manually roll back by redeploying the previous model version

Deploy the new model version alongside the current one, and use Amazon SageMaker’s multi- model endpoint to serve both models simultaneously, splitting traffic between them

16. You are a data scientist at a marketing agency tasked with creating a sentiment analysis model to analyze customer reviews for a new product. The company wants to quickly deploy a solution with minimal training time and development effort. You decide to leverage a pre-trained natural language processing (NLP) model and fine-tune it using a custom dataset of labeled customer reviews. Your team has access to both Amazon Bedrock and SageMaker JumpStart.

Which approach is the MOST APPROPRIATE for fine-tuning the pre-trained model with your custom dataset?

Use SageMaker JumpStart to create a custom container for your pre-trained model and manually implement fine-tuning with TensorFlow

Use SageMaker JumpStart to deploy a pre-trained NLP model and use the built-in fine-tuning functionality with your custom dataset to create a customized sentiment analysis model

Use Amazon Bedrock to train a model from scratch using your custom dataset, as Bedrock is optimized for training large models efficiently

Use Amazon Bedrock to select a foundation model from a third-party provider, then fine-tune the model directly in the Bedrock interface using your custom dataset

17. You are a machine learning engineer at a healthcare company responsible for developing and deploying an end-to-end ML workflow for predicting patient readmission rates. The workflow involves data preprocessing, model training, hyperparameter tuning, and deployment. Additionally, the solution must support regular retraining of the model as new data becomes available, with minimal manual intervention. You need to select the right solution to orchestrate this workflow efficiently while ensuring scalability, reliability, and ease of management.

Given these requirements, which of the following options is the MOST SUITABLE for orchestrating your ML workflow?

Implement the entire ML workflow using Amazon SageMaker Pipelines, which provides integrated orchestration for data processing, model training, tuning, and deployment

Use AWS Step Functions to define and orchestrate each step of the ML workflow, integrate with SageMaker for model training and deployment, and leverage AWS Lambda for data preprocessing tasks

Leverage Amazon EC2 instances to manually execute each step of the ML workflow, use Amazon RDS for storing intermediate results, and deploy the model using Amazon SageMaker endpoints

Use AWS Glue for data preprocessing, Amazon SageMaker for model training and tuning, and manually deploy the model to an Amazon EC2 instance for inference

18. You are a data scientist at an insurance company developing a machine learning model to predict the likelihood of claims being fraudulent. The company has a strong commitment to fairness and wants to ensure that the model does not disproportionately affect any specific demographic group. You decide to use Amazon SageMaker Clarify to assess potential bias in your model. In particular, you are interested in understanding how the model’s predictions differ across demographic groups when conditioned on relevant factors like income level, which could influence the likelihood of fraudulent claims.

Given this scenario, which of the following BEST describes how Conditional Demographic Disparity (CDD) can be used to assess and mitigate bias in your model?

CDD evaluates the disparity in positive prediction rates across demographic groups, conditioned on a specific feature like income, to detect bias that may not be apparent when only considering overall outcomes

CDD measures the difference in average predicted outcomes between demographic groups, helping to identify overall bias without considering other factors

CDD assesses the proportion of correctly predicted outcomes for each demographic group, helping to ensure that the model is equally accurate across groups

CDD focuses on the relationship between feature importance and demographic groups, highlighting whether certain features disproportionately influence predictions for specific groups

19. You are a machine learning engineer at a biotech company developing a custom deep learning model for analyzing genomic data. The model relies on a specific version of TensorFlow with custom Python libraries and dependencies that are not available in the standard SageMaker environments. To ensure compatibility and flexibility, you decide to use the "Bring Your Own Container" (BYOC) approach with Amazon SageMaker for both training and inference.

Given this scenario, which steps are MOST IMPORTANT for successfully deploying your custom container with SageMaker, ensuring that it meets the company’s requirements?

Package the model as a SageMaker-compatible file, upload it to Amazon S3, and use a pre-built SageMaker container for training, ensuring that the training job uses the custom environment

Create a Docker container with the required environment, push the container image to Amazon ECR (Elastic Container Registry), and use SageMaker’s Script Mode to execute the training script within the container

Build a Docker container with the required TensorFlow version and dependencies, push the container image to Docker Hub, and reference the image in SageMaker when creating the training job

Deploy the model locally using Docker, then use the AWS Management Console to manually copy the environment and model files to a SageMaker instance for training

20. You are a data scientist at a financial technology company developing a fraud detection system. The system needs to identify fraudulent transactions in real-time based on patterns in transaction data, including amounts, locations, times, and account histories. The dataset is large and highly imbalanced, with only a small percentage of transactions labeled as fraudulent. Your team has access to Amazon SageMaker and is considering various built-in algorithms to build the model.

Given the need for both high accuracy and the ability to handle imbalanced data, which SageMaker built-in algorithm is the MOST SUITABLE for this use case?

Implement the K-Nearest Neighbors (k-NN) algorithm to classify transactions based on similarity to known fraudulent cases

Apply the XGBoost algorithm with a custom objective function to optimize for precision and recall

Select the Random Cut Forest (RCF) algorithm for its ability to detect anomalies in transaction data

Use the Linear Learner algorithm with weighted classification to address the class imbalance

21. The fraud detection model is a large model and needs to be integrated into serverless applications to minimize infrastructure management.

Which of the following deployment targets should you choose for the different machine learning models, given their specific requirements? (Select two)

Choose Amazon Elastic Container Service (Amazon ECS) for the recommendation model, as it provides container orchestration for large-scale, batch processing workloads with tight integration into other AWS services

Use AWS Lambda to deploy the fraud detection model, which requires rapid scaling and integration into an existing serverless architecture, minimizing infrastructure management

Deploy the real-time recommendation model using Amazon SageMaker endpoints to ensure low-latency, high-availability, and managed infrastructure for real-time inference

Deploy the generative AI model using Amazon Elastic Kubernetes Service (Amazon EKS) to leverage containerized microservices for high scalability and control over the deployment environment

Deploy all models using Amazon SageMaker endpoints for consistency and ease of management, regardless of their individual requirements for scalability, latency, or integration

22. Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?

Amazon SageMaker Ground Truth

Amazon SageMaker Feature Store

Amazon SageMaker Clarify

Amazon SageMaker Data Wrangler

23. You are a Data Scientist working for an e-commerce company that is developing a machine learning model to predict whether a customer will make a purchase based on their browsing behavior. You need to evaluate the model's performance using different evaluation metrics to understand how well the model is predicting the positive class (i.e., customers who will make a purchase). The dataset is imbalanced, with a small percentage of customers making a purchase. Given this context, you must decide on the most appropriate evaluation techniques to assess your model's effectiveness and identify potential areas for improvement.

Which of the following evaluation techniques and metrics should you prioritize when assessing the performance of your model, considering the dataset's imbalance and the need for a comprehensive understanding of both false positives and false negatives? (Select two)

Prioritize Root mean squared error (RMSE) as the key metric, as it measures the average magnitude of the errors between predicted and actual values

Utilize the AUC-ROC curve to evaluate the model’s ability to distinguish between classes across various thresholds, particularly in the presence of class imbalance

Evaluate the model using the confusion matrix, which provides insights into true positives, false positives, true negatives, and false negatives, allowing you to calculate additional metrics such as precision, recall, and F1 score

Use accuracy as the primary metric, as it measures the percentage of correct predictions out of all predictions made by the model

Use precision and recall to focus on the model's ability to correctly identify positive cases while minimizing false positives and false negatives

24. You are a machine learning engineer working for a telecommunications company that needs to develop a predictive maintenance model. The goal is to predict when network equipment is likely to fail based on historical sensor data. The data includes features such as temperature, pressure, usage, and error rates recorded over time. The company wants to avoid unplanned downtime and optimize maintenance schedules by predicting failures just in time.

Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?

DeepAR Algorithm to forecast future equipment failures based on historical data

Time Series K-Means Algorithm to cluster similar patterns in the sensor data and predict failures

Random Cut Forest (RCF) Algorithm to detect anomalies in sensor data that may indicate impending failures

Linear Learner Algorithm to classify equipment status as 'healthy' or 'at risk' based on sensor readings

25. Which benefits might persuade a developer to choose a transparent and explainable machine learning model? (Select two)

They foster trust and confidence in model predictions

They enhance security by concealing model logic

They simplify the integration process with other systems

They facilitate easier debugging and optimization

They require less computational power and storage

26. You are a data scientist working for a financial institution that uses a machine learning model to predict loan defaults. The model was trained on historical data from the past five years, but after being deployed for several months, its accuracy has gradually decreased. Upon investigation, you suspect that the underlying data distribution has changed due to economic shifts and changes in customer behavior. This phenomenon is known as model drift, and you need to address it to ensure the model continues to perform well.

Which of the following approaches would you combine for detecting and managing drift in your ML model? (Select two)

Decrease the complexity of the model by removing features and layers, thereby turning it into a simpler model that can various types of data distributions

Increase the complexity of the model by adding more features and deeper layers, ensuring it can adapt to changing data distributions over time

Deploy a secondary model trained on different data and compare its predictions with the original model to detect any significant differences, indicating potential drift

Retrain the model on the most recent data to ensure it captures current trends, and use model versioning to track performance improvements over time

Implement continuous monitoring of input data features and model predictions using statistical tests to detect shifts in data distribution or performance, triggering an alert when drift is detected

27. You are a data scientist at a retail company responsible for deploying a machine learning model that predicts customer purchase behavior. The model needs to serve real-time predictions with low latency to support the company’s recommendation engine on its e-commerce platform. The deployment solution must also be scalable to handle varying traffic loads during peak shopping periods, such as Black Friday and holiday sales. Additionally, you need to monitor the model's performance and automatically roll out updates when a new version of the model is available.

Given these requirements, which AWS deployment service and configuration is the MOST SUITABLE for deploying the machine learning model?

Deploy the model on Amazon EC2 instances with a load balancer to distribute traffic, manually scaling the instances based on expected traffic during peak periods

Deploy the model on Amazon SageMaker with batch transform jobs, running the jobs periodically to generate predictions and storing the results in Amazon S3 for the recommendation engine

Deploy the model using Amazon SageMaker real-time hosting services with an auto-scaling endpoint, enabling you to automatically adjust the number of instances based on traffic demand

Use AWS Lambda to deploy the model as a serverless function, automatically scaling based on the number of requests, and store the model artifacts in Amazon S3

28. You are an ML engineer at a startup that is developing a recommendation engine for an e-commerce platform. The workload involves training models on large datasets and deploying them to serve real-time recommendations to customers. The training jobs are sporadic but require significant computational power, while the inference workloads must handle varying traffic throughout the day. The company is cost-conscious and aims to balance cost efficiency with the need for scalability and performance. Given these requirements, which approach to resource allocation is the MOST SUITABLE for training and inference, and why?

Use on-demand instances for both training and inference to ensure that the company only pays for the compute resources it uses when it needs them, avoiding any upfront commitments

Use on-demand instances for training, allowing the flexibility to scale resources as needed, and use provisioned resources with auto-scaling for inference to handle varying traffic while controlling costs

Use provisioned resources with spot instances for both training and inference to take advantage of the lowest possible costs, accepting the potential for interruptions during workload execution

Use provisioned resources with reserved instances for both training and inference to lock in lower costs and guarantee resource availability, ensuring predictability in budgeting

29. You are an ML engineer at a retail company that uses a SageMaker model to generate product recommendations for customers in real-time. During peak shopping periods, the traffic to the recommendation engine increases dramatically. The company needs to ensure that the model endpoint can handle these spikes in demand without compromising on response time or customer experience. At the same time, you want to optimize costs by scaling down resources during periods of low demand. You are evaluating different scaling policies to manage this dynamic workload effectively.

Which scaling policy is the MOST SUITABLE for this scenario, and why?

Use a manual scaling policy where you adjust the number of instances based on real-time monitoring of traffic, allowing you to fine-tune resource allocation as needed during high-demand periods

Use scheduled scaling to preemptively add or remove instances based on anticipated traffic patterns, such as known peak times during Black Friday, to ensure sufficient capacity is available when needed

Use a target tracking scaling policy that automatically adjusts the number of instances based on a predefined target metric, such as CPU utilization or invocations per instance, to maintain a steady level of performance during traffic spikes

Use a step scaling policy that adjusts the number of instances based on the size of the traffic spike, adding a set number of instances for each level of increased demand

30. You are an ML engineer at a data analytics company tasked with training a deep learning model on a large, computationally intensive dataset. The training job can tolerate interruptions and is expected to run for several hours or even days, depending on the available compute resources. The company has a limited budget for cloud infrastructure, so you need to minimize costs as much as possible.

Which strategy is the MOST EFFECTIVE for your ML training job while minimizing cost and ensuring the job completes successfully?

Start the training job using only Spot Instances to minimize cost, and switch to On-Demand instances manually if any Spot Instances are interrupted during training

Use Amazon SageMaker Managed Spot Training to dynamically allocate Spot Instances for the training job, automatically retrying any interrupted instances via checkpoints

Deploy the training job on a fixed number of On-Demand EC2 instances to ensure stability, and manually add Spot Instances as needed to speed up the job during off-peak hours

Use Amazon EC2 Auto Scaling to automatically add Spot Instances to the training job based on demand, and configure the job to continue processing even if some Spot Instances are interrupted

31. A company specializes in providing personalized product recommendations for e-commerce platforms. You’ve been tasked with developing a solution that can quickly generate high-quality product descriptions, tailor marketing copy based on customer preferences, and analyze customer reviews to identify trends in sentiment. Given the scale of data and the need for flexibility in choosing foundational models, you decide to use an AI service that can integrate seamlessly with your existing AWS infrastructure while also offering managed foundational models from third-party providers.

Which AWS service would best meet your requirements?

Amazon SageMaker

Amazon Rekognition

Amazon Bedrock

Amazon Personalize

32. You are a data scientist at a financial institution tasked with building a model to detect fraudulent transactions. The dataset is highly imbalanced, with only a small percentage of transactions being fraudulent. After experimenting with several models, you decide to implement a boosting technique to improve the model’s accuracy, particularly on the minority class. You are considering different types of boosting, including Adaptive Boosting (AdaBoost), Gradient Boosting, and Extreme Gradient Boosting (XGBoost).

Given the problem context and the need to effectively handle class imbalance, which boosting technique is MOST SUITABLE for this scenario?

Use Adaptive Boosting (AdaBoost) to focus on correcting the errors of weak classifiers, giving more weight to incorrectly classified instances during each iteration

Apply Extreme Gradient Boosting (XGBoost) for its ability to handle imbalanced datasets effectively through regularization, weighted classes, and optimized computational efficiency

Use Gradient Boosting and manually adjust the learning rate and class weights to improve performance on the minority class, avoiding the complexities of XGBoost

Implement Gradient Boosting to sequentially train weak learners, using the gradient of the loss function to improve performance on the minority class

33. You are a data scientist working on a binary classification model to predict whether customers will default on their loans. The dataset is highly imbalanced, with only 10% of the customers having defaulted in the past. After training the model, you need to evaluate its performance to ensure it effectively distinguishes between defaulters and non-defaulters. Given the class imbalance, accuracy alone is not sufficient to assess the model’s performance. Instead, you decide to use the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) to evaluate the model.

Which of the following interpretations of the ROC and AUC metrics is MOST ACCURATE for assessing the model’s performance?

A ROC curve that is closer to the top-left corner of the plot (AUC ~ 1) shows that the model is overfitting, and its predictions are too optimistic

An AUC close to 0 indicates that the model is highly accurate, correctly classifying almost all instances of defaulters and non-defaulters

An AUC close to 1.0 indicates that the model has excellent discriminatory power, effectively distinguishing between defaulters and non-defaulters

A ROC curve that is close to the diagonal line (AUC ~ 0.5) indicates that the model performs well across all thresholds

34. You are working as a data scientist at a financial services company tasked with developing a credit risk prediction model. After experimenting with several models, including logistic regression, decision trees, and support vector machines, you find that none of the models individually achieves the desired level of accuracy and robustness. Your goal is to improve overall model performance by combining these models in a way that leverages their strengths while minimizing their weaknesses.

Given the scenario, which of the following approaches is the MOST LIKELY to improve the model’s performance?

Apply stacking, where the predictions from logistic regression, decision trees, and support vector machines are used as inputs to a meta-model, such as a random forest, to make the final prediction

Implement boosting by training sequentially different types of models - logistic regression, decision trees, and support vector machines - where each new model corrects the errors of the previous ones

Use a simple voting ensemble, where the final prediction is based on the majority vote from the logistic regression, decision tree, and support vector machine models

Use bagging, where different types of models - logistic regression, decision trees, and support vector machines - are trained on different subsets of the data, and their predictions are averaged to produce the final result

35. You are a data scientist working on a deep learning model to classify medical images for disease detection. The model initially shows high accuracy on the training data but performs poorly on the validation set, indicating signs of overfitting. The dataset is limited in size, and the model is complex, with many parameters. To improve generalization and reduce overfitting, you need to implement appropriate techniques while balancing model complexity and performance.

Given these challenges, which combination of techniques is the MOST LIKELY to help prevent overfitting and improve the model’s performance on unseen data?

Prune the model by removing less important layers and nodes, and use L2 regularization to reduce the magnitude of the model’s weights, preventing overfitting

Use ensembling by combining multiple versions of the same model trained with different random seeds, and apply data augmentation to artificially increase the size of the dataset

Combine data augmentation to increase the diversity of the training data with early stopping to prevent overfitting, and use ensembling to average predictions from multiple models

Apply early stopping to halt training when the validation loss stops improving, and use dropout as a regularization technique to prevent the model from becoming too reliant on specific neurons

36. You are a data scientist at a financial services company tasked with deploying a lightweight machine learning model that predicts creditworthiness based on a customer’s transaction history. The model needs to provide real-time predictions with minimal latency, and the traffic pattern is unpredictable, with occasional spikes during business hours. The company is cost-conscious and prefers a serverless architecture to minimize infrastructure management overhead.

Which approach is the MOST SUITABLE for deploying this solution, and why?

Deploy the model directly within AWS Lambda as a function, and expose it through an API Gateway endpoint, allowing the function to scale automatically with traffic and provide real-time predictions

Deploy the model as a SageMaker endpoint for real-time inference, and configure AWS Lambda to preprocess incoming requests before sending them to the SageMaker endpoint for prediction

Deploy the model using Amazon ECS (Elastic Container Service) and configure an AWS Lambda to trigger the ECS service on-demand, ensuring that the model is only running during peak traffic periods

Use an Amazon EC2 instance to host the model, with AWS Lambda functions handling the communication between the API Gateway and the EC2 instance for prediction requests

37. How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?

K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions

K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data

K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering

K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples

38. A company has recently migrated to AWS Cloud and it wants to optimize the hardware used for its AI workflows.

Which of the following would you suggest?

Leverage either AWS Trainium or AWS Inferentia for the deep learning (DL) and generative AI inference applications

Leverage AWS Trainium for high-performance, cost-effective Deep Learning training. Leverage AWS Inferentia for the deep learning (DL) and generative AI inference applications

Leverage either AWS Trainium or AWS Inferentia for high-performance, cost-effective Deep Learning training

Leverage AWS Inferentia for high-performance, cost-effective Deep Learning training. Leverage AWS Trainium for the deep learning (DL) and generative AI inference applications

39. You are a machine learning engineer at a fintech company that has developed several models for various use cases, including fraud detection, credit scoring, and personalized marketing. Each model has different performance and deployment requirements. The fraud detection model requires real-time predictions with low latency and needs to scale quickly based on incoming transaction volumes. The credit scoring model is computationally intensive but can tolerate batch processing with slightly higher latency. The personalized marketing model needs to be triggered by events and doesn’t require constant availability.

Given these varying requirements, which deployment target is the MOST SUITABLE for each model?

Deploy the fraud detection model using AWS Lambda for serverless, on-demand execution, deploy the credit scoring model on Amazon EKS for scalable batch processing, and deploy the personalized marketing model on SageMaker endpoints to handle event-driven inference

Deploy the fraud detection model using SageMaker endpoints for low-latency, real-time predictions, deploy the credit scoring model on Amazon ECS for batch processing, and deploy the personalized marketing model using AWS Lambda for event-driven execution

Deploy all three models on a single Amazon EKS cluster to take advantage of Kubernetes orchestration, ensuring consistent management and scaling across different use cases

Deploy the fraud detection model on Amazon ECS for auto-scaling based on demand, deploy the credit scoring model using SageMaker endpoints for real-time scoring, and deploy the personalized marketing model on Amazon EKS for event-driven processing

40. Your data science team is working on developing a machine learning model to predict customer churn. The dataset that you are using contains hundreds of features, but you suspect that not all of these features are equally important for the model's accuracy. To improve the model's performance and reduce its complexity, the team wants to focus on selecting only the most relevant features that contribute significantly to minimizing the model's error rate.

Which feature engineering process should your team apply to select a subset of features that are the most relevant towards minimizing the error rate of the trained model?

Feature extraction

Feature creation

Feature transformation

Feature selection

41. You are a DevOps engineer at a tech company that is building a scalable microservices-based application. The application is composed of several containerized services, each responsible for different parts of the application, such as user authentication, data processing, and recommendation systems. The company wants to standardize and automate the deployment and management of its infrastructure using Infrastructure as Code (IaC). You need to choose between AWS CloudFormation and AWS Cloud Development Kit (CDK) for defining the infrastructure. Additionally, you must decide on the appropriate AWS container service to manage and deploy these microservices efficiently.

Given the requirements, which combination of IaC option and container service is MOST SUITABLE for this scenario, and why?

Use AWS CloudFormation with YAML templates for infrastructure automation and deploy the containerized microservices using Amazon Lightsail Containers to simplify management and reduce costs

Use AWS CloudFormation to define and deploy the infrastructure as code, and Amazon ECR (Elastic Container Registry) with Fargate for running the containerized microservices without needing to manage the underlying servers

Use AWS CDK for infrastructure as code, allowing you to define the infrastructure in a high-level programming language, and deploy the containerized microservices using Amazon EKS (Elastic Kubernetes Service) for advanced orchestration and scalability

Use AWS CDK with Amazon ECS on EC2 instances to combine the flexibility of programming languages with direct control over the underlying server infrastructure for the microservices

42. You are an ML Engineer at a financial services company tasked with deploying a machine learning model for real-time fraud detection in production. The model requires low-latency inference to ensure that fraudulent transactions are flagged immediately. However, you also need to conduct extensive testing and experimentation in a separate environment to fine-tune the model and validate its performance before deploying it. You must provision compute resources that are appropriate for both environments, balancing performance, cost, and the specific needs of testing and production.

Which of the following strategies should you implement to effectively provision compute resources for both the production environment and the test environment using Amazon SageMaker, considering the different requirements for each environment? (Select two)

Leverage AWS Inferentia accelerators in the production environment to meet high throughput and low latency requirements

Provision CPU-based instances in both production and test environments to reduce costs, as CPU instances are generally cheaper than GPU instances

Use GPU-based instances in both production and test environments to ensure that the model inference and testing are both performed at maximum speed, regardless of cost

Use CPU-based instances in the test environment to save on costs during experimentation

Provision identical instances in both production and test environments to ensure consistent performance between the two, eliminating the risk of discrepancies during deployment

43. You are a data scientist at an insurance company that uses a machine learning model to assess the risk of potential clients and set insurance premiums accordingly. The model was trained on data from the past few years, but recently, the company has expanded its services to new regions with different demographic characteristics. You are concerned that these changes in the data distribution might affect the model's performance and lead to biased or inaccurate predictions. To address this, you decide to use Amazon SageMaker Clarify to monitor and detect any significant shifts in data distribution that could impact the model.

Which of the following actions is the MOST EFFECTIVE for detecting changes in data distribution using SageMaker Clarify and mitigating their impact on model performance?

Set up a continuous monitoring job with SageMaker Clarify to track changes in feature distribution over time and alert you when a significant feature attribution drift is detected, allowing you to investigate and potentially retrain the model

Implement a random sampling process to manually review a subset of incoming data each month, comparing it with the original training data to check for distribution changes

Use SageMaker Clarify’s bias detection capabilities to analyze the model’s output and identify any disparities between different demographic groups, retraining the model only if significant bias is detected

Use SageMaker Clarify to perform a one-time bias analysis during model training, ensuring that the model is initially fair and accurate, and manually monitor future data distribution changes

44. What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?

Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always required

Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization

Feature engineering tasks for structured data and unstructured data are identical and do not vary based on data type

Feature engineering for structured data focuses on image recognition, whereas for unstructured data, it focuses on numerical data analysis

45. A company uses a generative model to analyze animal images in the training dataset to record variables like different ear shapes, eye shapes, tail features, and skin patterns.

Which of the following tasks can the generative model perform?

The model can classify multiple species of animals such as cats, dogs, etc

The model can recreate new animal images that were not in the training dataset

The model can identify any image from the training dataset

The model can classify a single species of animals such as cats

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