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AIF-C01 exam dumps: questions 201 to 230 of 230

Page 3 of the free AIF-C01 question bank for the AWS Certified AI Practitioner exam. Questions 201 to 230 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated December 2024

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Free AIF-C01 practice questions

Questions 201 to 205 of 230

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

AIF-C01 Question 201

Select 3

A company is deploying a machine learning model on AWS to process sensitive customer data. They want to ensure the AI system is secure by restricting network access, encrypting data at rest, and monitoring for sensitive data leakage. Which combination of AWS services and features should the company use?

A
AWS PrivateLink to restrict network access to the model endpoint
B
Amazon Macie to monitor and discover sensitive data
C
AWS Key Management Service (KMS) to manage encryption keys for data at rest
D
AWS Lambda to automatically scale the model endpoint
E
Amazon S3 Transfer Acceleration to speed up data uploads

Show answer and explanation

Correct answers: A, B, C

Explanation

To secure an AI system processing sensitive data, you need to address network security, data encryption, and data monitoring. AWS PrivateLink ensures private network access, Amazon Macie helps detect and monitor sensitive data, and AWS KMS enables encryption of data at rest. These services collectively provide a robust security framework for the AI system.

A. Correct.
AWS PrivateLink is designed to restrict network access by enabling private connectivity between VPCs and AWS services without exposing traffic to the public internet. This ensures the AI system's network traffic is secure.
B. Correct.
Amazon Macie is a data security service that uses machine learning to discover, classify, and protect sensitive data. It is ideal for monitoring sensitive data leakage in this scenario.
C. Correct.
AWS Key Management Service (KMS) enables encryption and secure management of keys for sensitive data at rest, ensuring data security.
D. Incorrect.
AWS Lambda is a serverless compute service that is not directly related to securing an AI system. Instead, it is used for event-driven computing and scaling applications.
E. Incorrect.
Amazon S3 Transfer Acceleration speeds up data uploads but does not provide security features like network restriction, encryption, or sensitive data monitoring.

AIF-C01 Question 202

Select 3

Your company is deploying a machine learning (ML) model on Amazon SageMaker, and you are tasked with ensuring the system is secure. The model processes sensitive customer data, and compliance requirements mandate data encryption at rest, controlled network access, and proper IAM permissions. Which of the following AWS services and features should you use to secure the AI system?

A
Use AWS Key Management Service (AWS KMS) to encrypt data at rest.
B
Set up an Amazon Macie job to find and classify sensitive data in Amazon S3.
C
Use AWS PrivateLink to restrict network traffic between SageMaker and other services.
D
Grant Amazon SageMaker full administrator access to simplify permissions management.
E
Create an IAM role with least privilege access for SageMaker to access only required resources.

Show answer and explanation

Correct answers: A, C, E

Explanation

To secure an AI system in AWS, it is important to implement encryption (using AWS KMS), control network access (using AWS PrivateLink), and follow IAM best practices like least privilege access. These measures collectively reduce the risk of unauthorized access and ensure compliance with security and regulatory requirements. While Amazon Macie can help identify sensitive data, it does not directly secure the AI system, and granting full administrator access is not aligned with security best practices.

A. Correct.
Correct. AWS KMS allows you to encrypt sensitive data at rest, which is a critical security measure for compliance and data protection.
B. Incorrect.
Partially correct but not sufficient for this scenario. Amazon Macie is useful for discovering and classifying sensitive data, but it does not directly secure the SageMaker deployment.
C. Correct.
Correct. AWS PrivateLink helps secure communications by restricting network traffic to private endpoints, reducing exposure to the public internet.
D. Incorrect.
Incorrect. Granting full administrator access violates the principle of least privilege and increases the risk of accidental or malicious misuse.
E. Correct.
Correct. Creating an IAM role with least privilege ensures SageMaker has access to only the resources it needs, which is a best practice for securing AI systems.

AIF-C01 Question 203

Select 3

You are designing an AI system hosted on AWS, which processes sensitive customer data. To ensure the system complies with security and privacy requirements, you need to implement measures to secure the data and restrict unauthorized access. Which of the following actions should you take to secure this AI system?

A
Use IAM roles with least privilege to grant access to AI system resources.
B
Enable AWS PrivateLink to ensure communication between AI components stays within the AWS network.
C
Store sensitive data in Amazon S3 without encryption for faster processing.
D
Use Amazon Macie to identify and protect sensitive data in your storage.
E
Rely entirely on AWS to secure your AI system as part of the shared responsibility model.

Show answer and explanation

Correct answers: A, B, D

Explanation

To secure AI systems on AWS, you must implement security best practices such as using IAM roles with least privilege, AWS PrivateLink for secure communication, and tools like Amazon Macie for identifying and protecting sensitive data. While AWS manages the security of the underlying infrastructure, you are responsible for securing your data and applications as part of the shared responsibility model.

A. Correct.
Correct: IAM roles with least privilege ensure users and services have only the necessary permissions to access resources, reducing the risk of unauthorized access.
B. Correct.
Correct: AWS PrivateLink ensures secure communication between AI components by keeping data within the AWS network, minimizing exposure to the public internet.
C. Incorrect.
Incorrect: Storing sensitive data without encryption compromises the security of your system, making it vulnerable to potential breaches.
D. Correct.
Correct: Amazon Macie helps identify sensitive data like personally identifiable information (PII) and ensures it is protected in compliance with security and privacy regulations.
E. Incorrect.
Incorrect: The AWS shared responsibility model requires you to secure your applications and data. AWS secures the underlying infrastructure, but you must implement security measures for your AI system.

AIF-C01 Question 204

Select 3

A company is building a machine learning model for predicting customer churn and wants to ensure transparency and accountability in their process. Which of the following AWS features can help the company document the data origins, transformations, and the model’s metadata effectively?

A
AWS Glue Data Catalog
B
Amazon SageMaker Model Cards
C
Amazon Rekognition
D
AWS CloudTrail
E
Amazon SageMaker Data Wrangler

Show answer and explanation

Correct answers: A, B, E

Explanation

Transparency and accountability in machine learning require proper documentation of data origins (data lineage) and metadata about the models. AWS Glue Data Catalog helps track data assets and their lineage, while SageMaker Model Cards focus on documenting model details, including the data used. Data Wrangler aids in documenting data preparation steps. Together, these tools help ensure a robust documentation process for machine learning workflows.

A. Correct.
AWS Glue Data Catalog helps maintain a centralized repository of metadata about data assets, including their origins (data lineage). This supports documenting data sources and transformations.
B. Correct.
Amazon SageMaker Model Cards provide a structured way to document information about machine learning models, including data origins, performance metrics, and intended use cases.
C. Incorrect.
Amazon Rekognition is an AI service for image and video analysis. While it provides insights into media content, it does not support documenting data origins or metadata.
D. Incorrect.
AWS CloudTrail tracks API activity and resource changes in AWS accounts but is not designed for documenting data origins or metadata related to machine learning models.
E. Correct.
Amazon SageMaker Data Wrangler simplifies data preparation and transformation steps, allowing users to document these steps for transparency and reproducibility.

AIF-C01 Question 205

Select 3

A company is building a machine learning model using Amazon SageMaker. The data science team wants to ensure that they can effectively track the origins of their datasets, transformations applied, and the model's training history. Which combination of tools or practices should they use to achieve this?

A
Use SageMaker Model Cards to document the model's training history and data sources.
B
Maintain a manual spreadsheet to document data transformations and sources.
C
Implement a data catalog using AWS Glue to track data origins and metadata.
D
Leverage SageMaker Lineage Tracking to monitor data and model dependencies.
E
Rely solely on the training script comments to document data origins.

Show answer and explanation

Correct answers: A, C, D

Explanation

To effectively track data origins and maintain transparency in machine learning workflows, it is essential to use integrated tools like SageMaker Model Cards, AWS Glue Data Catalog, and SageMaker Lineage Tracking. These tools provide structured and scalable solutions for documenting data lineage, transformations, and model histories. Manual methods or relying solely on training script comments are not sufficient for ensuring traceability and scalability.

A. Correct.
SageMaker Model Cards are used to document details about a model, such as its purpose, training datasets, evaluation metrics, and lineage. This is a key tool for tracking the model's history.
B. Incorrect.
While maintaining a manual spreadsheet can help with documentation, it is prone to errors, lacks automation, and does not scale well for tracking complex data and model dependencies.
C. Correct.
AWS Glue Data Catalog allows you to organize and track metadata for datasets, making it easier to identify the origins and structure of data used in machine learning pipelines.
D. Correct.
SageMaker Lineage Tracking is explicitly designed to track dependencies between datasets, transformations, and models, ensuring transparency and traceability.
E. Incorrect.
Training script comments are insufficient for comprehensive tracking of data origins and transformations as they are not structured or integrated with tools that manage lineage and metadata.

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AIF-C01 practice questions 201 to 230 of 230

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