Free Amazon MLA-C01 Übungsprüfungen

Question #1

Which of the following is correct regarding the training set, validation set, and test set used in the context of machine learning? (Select two)

A . Test set is used to determine how well the model generalizes
B . Test set is used for hyperparameter tuning
C . Test sets are optional
D . Validation sets are optional
E . Validation set is used to determine how well the model generalizes

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Question #2

You are a Cloud Financial Manager at a SaaS company that uses various AWS services to run its applications and machine learning workloads. Your management team has asked you to reduce overall AWS spending while ensuring that critical applications remain highly available and performant. To achieve this, you need to use AWS cost analysis tools to monitor spending, identify cost-saving opportunities, and optimize resource utilization across the organization.

Which of the following actions can you perform using the appropriate AWS cost analysis tools to achieve your goal of reducing costs and optimizing AWS resource utilization? (Select two)

A . Use AWS Cost Explorer to analyze historical spending patterns, identify cost trends, and forecast future costs to help with budgeting and planning
B . Use AWS Cost Explorer to automatically delete unused resources across your AWS environment, ensuring that no unnecessary costs are incurred
C . Leverage AWS Trusted Advisor to receive recommendations for cost optimization, such as identifying underutilized or idle resources, and reserved instance purchasing opportunities
D . Use AWS Cost Explorer to set custom budgets for cost and usage to govern costs across your organization and receive alerts when costs exceed your defined thresholds
E . Leverage AWS Trusted Advisor to directly modify and reconfigure resources based on cost optimization recommendations without manual intervention

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Richtige Antwort: A, C
A, C

Explanation:

Correct options:

Use AWS Cost Explorer to analyze historical spending patterns, identify cost trends, and forecast future costs to help with budgeting and planning

AWS Cost Explorer allows you to analyze your past AWS spending, identify cost trends, and forecast future costs based on historical data. This tool is valuable for budgeting and financial planning, helping you make informed decisions about resource allocation and cost management.

Leverage AWS Trusted Advisor to receive recommendations for cost optimization, such as identifying underutilized or idle resources, and reserved instance purchasing opportunities

AWS Trusted Advisor provides actionable recommendations to optimize your AWS environment, including cost-saving suggestions. It identifies opportunities such as underutilized resources, idle instances, and reserved instance purchasing options that can significantly reduce your AWS costs.

via – https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/billing-what-is.html

Incorrect options:

Use AWS Cost Explorer to set custom budgets for cost and usage to govern costs across your organization and receive alerts when costs exceed your defined thresholds You can only use AWS Budgets to set custom budgets for cost and usage to govern costs across your organization and receive alerts when costs exceed your defined thresholds.

Use AWS Cost Explorer to automatically delete unused resources across your AWS environment, ensuring that no unnecessary costs are incurred AWS Cost Explorer does not have the capability to automatically delete unused resources. It is a cost analysis tool that helps you visualize and understand your costs but does not manage or modify your AWS resources directly.

Leverage AWS Trusted Advisor to directly modify and reconfigure resources based on cost optimization recommendations without manual intervention – AWS Trusted Advisor provides recommendations but does not automatically modify or reconfigure resources. Changes must be made manually based on the insights provided by the tool.

References:

https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/billing-what-is.html

https://aws.amazon.com/aws-cost-management/aws-cost-explorer/

https://docs.aws.amazon.com/awssupport/latest/user/trusted-advisor.html

Question #3

You are preparing a dataset for training a machine learning model using SageMaker Data Wrangler. The dataset has several missing values spread across different columns, and these columns contain numeric data. Before training the model, it is essential to handle these missing values to ensure the model performs optimally. The goal is to replace the missing values in each numeric column with the mean of that column.

Which transformation in SageMaker Data Wrangler should you apply to replace the missing values in numeric columns with the mean of those columns?

A . Encode
B . Impute
C . Scale
D . Drop

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Question #4

What is uncertainty in the context of machine learning?

A . The clarity of the model’s decision-making process
B . The speed of the algorithm
C . The amount of data available for training
D . An imperfect outcome

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Question #5

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?

A . Amazon SageMaker Feature Store
B . Amazon SageMaker Data Wrangler
C . SageMaker Model Dashboard
D . Amazon SageMaker Clarify

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Richtige Antwort: B
B

Explanation:

Correct option:

Amazon SageMaker Data Wrangler:

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data that you want from various data sources and import it quickly. Next, you can use the data quality and insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations, so you can quickly transform data without writing code.

With the SageMaker Data Wrangler data selection tool, you can quickly access and select your tabular and image data from various popular sources – such as Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, AWS Lake Formation, Snowflake, and Databricks – and over 50 other third-party sources – such as Salesforce, SAP, Facebook Ads, and Google Analytics. You can also write queries for data sources using SQL and import data directly into SageMaker from various file formats, such as CSV, Parquet, JSON, and database tables.

How Data Wrangler works:

via – https://aws.amazon.com/sagemaker/data-wrangler/

Incorrect options:

SageMaker Model Dashboard – Amazon SageMaker Model Dashboard is a centralized portal, accessible from the SageMaker console, where you can view, search, and explore all of the models in your account. You can track which models are deployed for inference and if they are used in batch transform jobs or hosted on endpoints.

Amazon SageMaker Clarify – SageMaker Clarify helps identify potential bias during data preparation without writing code. You specify input features, such as gender or age, and SageMaker Clarify runs an analysis job to detect potential bias in those features.

Amazon SageMaker Feature Store – Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference.

Reference: https://aws.amazon.com/sagemaker/data-wrangler/

Question #6

You are a DevOps engineer responsible for maintaining a serverless machine learning application that provides real-time predictions using AWS Lambda. Recently, users have reported increased latency when interacting with the application, especially during peak usage hours. You need to quickly identify the root cause of the latency and resolve the performance issues to ensure the application remains responsive.

Which combination of monitoring and observability tools is the MOST EFFECTIVE for troubleshooting the latency and performance issues in this serverless application?

A . Use AWS X-Ray to trace requests across the entire application, identify bottlenecks, and visualize the end-to-end latency for each request. Combine this with Amazon CloudWatch Lambda Insights to monitor the Lambda function’s memory usage, CPU usage, and invocation times
B . Use Amazon CloudWatch Alarms to set thresholds for Lambda duration and error rates, and configure AWS X-Ray to periodically sample traces from the application for analysis
C . Enable detailed monitoring in Amazon CloudWatch to track Lambda invocations, errors, and throttles, and manually inspect the Lambda code to identify performance bottlenecks
D . Deploy Amazon CloudWatch Logs Insights to query and analyze the application logs for errors, and use AWS Config to review recent changes to the infrastructure that might have introduced latency

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Richtige Antwort: A
A

Explanation:

Correct option:

Use AWS X-Ray to trace requests across the entire application, identify bottlenecks, and visualize the end-to-end latency for each request. Combine this with Amazon CloudWatch Lambda Insights to monitor the Lambda function’s memory usage, CPU usage, and invocation times

This approach leverages AWS X-Ray to trace the entire request path, providing detailed insights into where latency occurs, whether in the Lambda function, external APIs, or other integrated services. AWS X-Ray’s visualization helps identify bottlenecks and latency sources.

via – https://docs.aws.amazon.com/xray/latest/devguide/xray-gettingstarted.html

Amazon CloudWatch Lambda Insights provides detailed metrics on Lambda function performance, including memory usage, CPU usage, and invocation times, allowing you to pinpoint performance issues specific to the Lambda environment. This combination is powerful for diagnosing and resolving latency issues in a serverless architecture.

via – https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Lambda-Insights.html

Incorrect options:

Enable detailed monitoring in Amazon CloudWatch to track Lambda invocations, errors, and throttles, and manually inspect the Lambda code to identify performance bottlenecks – Detailed monitoring in Amazon CloudWatch provides useful metrics, but it lacks the deep trace analysis that AWS X-Ray offers. Manually inspecting code is time-consuming and may not accurately identify the performance bottlenecks.

Deploy Amazon CloudWatch Logs Insights to query and analyze the application logs for errors, and use AWS Config to review recent changes to the infrastructure that might have introduced latency – Amazon CloudWatch Logs Insights is effective for analyzing logs, but it doesn’t provide the same end-to-end tracing capabilities as AWS X-Ray. AWS Config is useful for tracking infrastructure changes, but it may not directly help identify latency issues within the application.

Use Amazon CloudWatch Alarms to set thresholds for Lambda duration and error rates, and configure AWS X-Ray to periodically sample traces from the application for analysis – While setting alarms for Lambda duration and error rates is important, relying on periodically sampled traces with AWS X-Ray may miss intermittent issues. Continuous tracing and monitoring with AWS X-Ray and Lambda Insights offer a more comprehensive solution.

References:

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Lambda-Insights.html

https://docs.aws.amazon.com/xray/latest/devguide/xray-gettingstarted.html

https://aws.amazon.com/blogs/aws/aws-lambda-support-for-aws-x-ray/

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/cloudwatch-metrics-basic-detailed.html

Question #7

You are a Senior ML Engineer at a global logistics company that heavily relies on machine learning models for optimizing delivery routes, predicting demand, and detecting anomalies in real-time. The company is rapidly expanding, and you are tasked with building a maintainable, scalable, and cost-effective ML infrastructure that can handle increasing data volumes and evolving model requirements. You must implement best practices to ensure that the infrastructure can support ongoing development, deployment, monitoring, and scaling of multiple models across different regions.

Which of the following strategies should you implement to create a maintainable, scalable, and cost-effective ML infrastructure for your company using AWS services? (Select three)

A . Provision fixed resources for each model to avoid unexpected costs, ensuring that the infrastructure is always available for each model
B . Store all model artifacts and data in Amazon CodeCommit for version control and managing changes over time
C . Use a monolithic architecture to manage all machine learning models in a single environment, simplifying management and reducing overhead
D . Store all model artifacts and data in Amazon S3, and use versioning to manage changes over time, ensuring that models can be easily rolled back if needed
E . Implement a microservices-based architecture with Amazon SageMaker endpoints, where each model is deployed independently, allowing for isolated scaling and updates
F . Utilize infrastructure as code (IaC) with AWS CloudFormation to automate the deployment and management of ML resources, making it easy to replicate and scale infrastructure across regions

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Richtige Antwort: D, E, F
D, E, F

Explanation:

Correct options:

Implement a microservices-based architecture with Amazon SageMaker endpoints, where each model is deployed independently, allowing for isolated scaling and updates

A microservices-based architecture with Amazon SageMaker endpoints allows each model to be deployed, managed, and scaled independently. This approach enhances maintainability by isolating different components, making it easier to update models or scale specific services without affecting others. It also supports a more scalable and flexible infrastructure.

Utilize infrastructure as code (IaC) with AWS CloudFormation to automate the deployment and management of ML resources, making it easy to replicate and scale infrastructure across regions Utilizing infrastructure as code (IaC) with AWS CloudFormation enables you to automate the deployment and management of your ML infrastructure. This approach ensures consistency across environments, simplifies scaling, and allows for rapid deployment in multiple regions. IaC also enhances maintainability by providing a version-controlled, repeatable process for managing infrastructure changes.

Store all model artifacts and data in Amazon S3, and use versioning to manage changes over time, ensuring that models can be easily rolled back if needed

Storing model artifacts and data in Amazon S3 with versioning is a good practice for maintaining model history and enabling rollbacks.

Incorrect options:

Use a monolithic architecture to manage all machine learning models in a single environment, simplifying management and reducing overhead – A monolithic architecture can simplify management in the short term but becomes difficult to maintain and scale as the number of models and services grows. It also limits flexibility in updating or scaling individual models, leading to potential bottlenecks and higher costs. Provision fixed resources for each model to avoid unexpected costs, ensuring that the infrastructure is always available for each model – Provisioning fixed resources for each model may lead to underutilization or overprovisioning, resulting in higher costs. Dynamic resource allocation, such as using auto-scaling or spot instances, is generally more cost-effective and scalable.

Store all model artifacts and data in Amazon CodeCommit for version control and managing changes over time – Amazon CodeCommit is the right fit for code-specific version control. You should not use CodeCommit to store model related data.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cloudformation-overview.html

https://aws.amazon.com/codecommit/

Question #8

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?

A . 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
B . 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
C . 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
D . 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

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Richtige Antwort: B
B

Explanation:

Correct option:

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

Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker hosting services and get an endpoint that can be used for inference. These endpoints are fully managed and support autoscaling. SageMaker endpoints are optimized for low-latency, real-time predictions, making them ideal for the fraud detection model.

Amazon ECS provides a service scheduler for long-running tasks and applications. It also provides the ability to run standalone tasks or scheduled tasks for batch jobs or single run tasks. You can specify the task placement strategies and constraints for running tasks that best meet your needs. Amazon ECS is well-suited for batch processing tasks, making it a good choice for the credit scoring model.

AWS Lambda is ideal for the event-driven nature of the personalized marketing model, allowing it to scale on-demand with minimal cost.

via – https://docs.aws.amazon.com/AmazonECS/latest/developerguide/scheduling_tasks.html

Incorrect options:

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 – AWS Lambda is serverless and ideal for event-driven tasks, but it may not provide the low-latency, real-time performance required for fraud detection. SageMaker endpoints are better suited for this use case. The credit scoring model is better suited for ECS, where batch processing can be efficiently managed, while personalized marketing is a good fit for AWS Lambda.

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 – Deploying all models on a

single Amazon EKS cluster could be overkill and lead to unnecessary complexity. While Kubernetes provides powerful orchestration, it might be excessive for simple, event-driven or batch workloads.

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 – While Amazon ECS can handle auto-scaling, it is not as optimized for real-time, low-latency predictions as SageMaker endpoints. Additionally, using SageMaker endpoints for the credit scoring model does not align well with batch processing needs. The personalized marketing model is better suited to AWS Lambda rather than Amazon EKS, which is more complex and designed for containerized applications with continuous workloads.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html

https://docs.aws.amazon.com/AmazonECS/latest/developerguide/scheduling_tasks.html

Question #9

Why is it important to estimate the amount of uncertainty in an ML model?

A . To improve the model’s computational efficiency
B . To increase the speed of data processing
C . To reduce the amount of training data needed
D . To avoid potential misinterpretations of life and property

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Question #10

You are a data scientist working for an e-commerce company that wants to implement personalized product recommendations for its users. The company has a large dataset of user interactions, including clicks, purchases, and reviews. The goal is to create a recommendation system that can scale to millions of users while providing real-time recommendations based on user behavior. You need to choose the most appropriate built-in algorithm in Amazon SageMaker to achieve this goal.

Given the requirements, which of the following Amazon SageMaker built-in algorithms is the MOST SUITABLE for this use case?

A . XGBoost Algorithm to rank the products based on user behavior and demographic features
B . K-Means Algorithm to cluster users into segments and recommend products based on these segments
C . Factorization Machines Algorithm to model user-item interactions for collaborative filtering
D . BlazingText Algorithm to analyze the text in user reviews and identify product similarities

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Richtige Antwort: C
C

Explanation:

Correct option:

Factorization Machines Algorithm to model user-item interactions for collaborative filtering

The Factorization Machines algorithm is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the Factorization Machines model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation.

Factorization Machines is well-suited for collaborative filtering. It excels at modeling sparse user-item interactions, making it ideal for large-scale recommendation systems where there are many users and items but relatively few interactions for each user-item pair. This algorithm can effectively capture latent factors to provide personalized recommendations.

Mapping use cases to built-in algorithms:

via – https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

Incorrect options:

XGBoost Algorithm to rank the products based on user behavior and demographic features – XGBoost is a powerful algorithm for ranking and classification tasks, but it’s not optimized for collaborative filtering, which is crucial for personalized recommendations in this context.

BlazingText Algorithm to analyze the text in user reviews and identify product similarities – BlazingText is effective for text classification and word embedding but is not specifically designed for recommendation systems. While it can be used to analyze user reviews, it does not address the core requirement of user-item interaction modeling.

K-Means Algorithm to cluster users into segments and recommend products based on these segments – K-Means is useful for clustering users into segments, but this approach is more generalized and does not provide the level of personalization required for individual recommendations based on specific user-item interactions.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html

https://aws.amazon.com/blogs/machine-learning/accelerate-and-improve-recommender-system-training-and-predictions-using-amazon-sagemaker-feature-store/

https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html