AIF-C01 Practice Test 7 — 10 Questions

Question 1

Which THREE considerations are essential for ensuring responsible AI in a model that predicts employee performance? (Choose 3)

Accepted Answer

Incorporate human review before final decisions. Option C is correct because responsible AI in an HR context requires human-in-the-loop oversight: performance predictions should inform, not automate, decisions about employees, so a human reviewer can catch errors and provide context before any action is taken. Option D is correct because employee performance data is personal and often sensitive, so the model must comply with privacy and consent requirements (e.g., GDPR lawful basis, purpose limitation, and data minimization) to be ethically and legally sound. Option E is correct because bias testing across demographic groups (e.g., comparing error rates, selection rates, and disparate impact metrics by gender, age, or ethnicity) is essential to detect and mitigate discriminatory outcomes in performance predictions. Option A does not belong because minimizing features to cut cost is an efficiency concern, not a responsible-AI safeguard, and can even harm fairness if it removes relevant variables. Option B does not belong because publishing individual employees' predicted performance publicly would violate privacy and confidentiality rather than constitute meaningful transparency, which is better served by model documentation and explainability to authorized stakeholders.

Answer

Minimize the number of features to reduce cost

Answer

Publish the model's predictions publicly for transparency

Question 2

Refer to the exhibit. A developer runs the CLI command to summarize text using Claude v2 in Bedrock. The output is shorter than expected. Which change should the developer make to allow a longer response?

Accepted Answer

Increase 'max_tokens_to_sample' to 1000. The 'max_tokens_to_sample' parameter in the Bedrock InvokeModel API directly controls the maximum number of tokens the model can generate in its response. By default, this value is often set low (e.g., 256 tokens), which truncates the output. Increasing it to 1000 allows Claude v2 to produce a longer summary up to that token limit.

Answer

Change the prompt to include 'Write a long summary'

Answer

Set 'stop_reason' to 'none'

Answer

Use a different region like us-west-2

Question 3

An e-commerce company uses an Amazon Lex chatbot to handle customer inquiries. They want to implement human oversight for sensitive interactions, such as when the chatbot cannot provide a confident response. Which AWS service should they integrate?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is the correct service because it provides built-in human review workflows for ML predictions, allowing you to route low-confidence responses from Amazon Lex to human reviewers for oversight. This directly addresses the requirement for human oversight on sensitive interactions where the chatbot cannot provide a confident response.

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Amazon Rekognition

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Amazon Comprehend

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Amazon SageMaker Ground Truth

Question 4

A large enterprise has multiple teams deploying ML models on AWS. To ensure governance and accountability, they need to enforce that all models pass a fairness review before production deployment. Which SageMaker feature should they use to implement this approval workflow?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized catalog for managing ML models, including versioning, approval status, and metadata. It supports approval workflows by allowing you to define model groups, set approval statuses (e.g., PendingApproval, Approved, Rejected), and integrate with CI/CD pipelines to enforce that only approved models are deployed to production.

Answer

SageMaker Studio

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SageMaker Experiments

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SageMaker Model Monitor

Question 5

Which AWS service can be used to extract text and data from scanned documents such as invoices and receipts?

Accepted Answer

Amazon Textract. Amazon Textract is a machine learning service specifically designed to extract text, handwriting, and structured data (like tables and forms) from scanned documents. It goes beyond simple optical character recognition (OCR) by using ML models to understand the layout and relationships within documents, making it ideal for processing invoices and receipts.

Answer

Amazon Transcribe

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Question 6

A media company uses Amazon Transcribe for automatic speech recognition. They discover the model has higher error rates for non-native English speakers. Which Responsible AI principle are they failing to uphold?

Accepted Answer

Fairness. The model's higher error rates for non-native English speakers indicate a bias in the training data or model design that leads to disparate performance across demographic groups. This directly violates the Fairness principle of Responsible AI, which requires that AI systems treat all groups equitably and do not amplify existing societal biases. Amazon Transcribe's underlying acoustic and language models may have been trained predominantly on native English speech, causing systematic underperformance for non-native accents.

Answer

Explainability

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Robustness

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Privacy

Question 7

A team is using Amazon Bedrock to generate images from text prompts. The generated images often contain artifacts and do not match the prompt description. Which combination of steps should the team take to improve image quality?

Accepted Answer

Refine the prompt with more descriptive language and adjust the CFG scale and inference steps.. Refining the prompt with more descriptive language helps the model better interpret the user's intent, while adjusting the CFG (Classifier-Free Guidance) scale controls how strictly the model adheres to the prompt, and increasing inference steps allows the diffusion process to produce higher-quality, artifact-free images. These are standard hyperparameters in diffusion-based image generation models on Amazon Bedrock, directly addressing both artifacts and prompt mismatch.

Answer

Fine-tune the model using SageMaker Ground Truth and increase the training epochs.

Answer

Increase the max token count and use a larger model variant.

Answer

Use a different foundation model and increase the image resolution.

Question 8

A company uses Amazon Bedrock to generate product descriptions. They notice that the output sometimes contains incorrect information. What should they do to improve accuracy?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) enhances the accuracy of foundation model outputs by grounding the generation in authoritative, up-to-date external knowledge sources. Instead of relying solely on the model's parametric memory, RAG retrieves relevant documents or data from a vector database (e.g., Amazon OpenSearch Serverless) and injects them into the prompt context, reducing hallucinations and incorrect information in product descriptions.

Answer

Increase the temperature parameter.

Answer

Use a larger foundation model.

Answer

Use AWS WAF to filter outputs.

Question 9

Which of the following correctly describes the purpose of pre-training in the context of large language models?

Accepted Answer

To learn general language representations from a large, unlabeled corpus. Pre-training in large language models involves learning general language representations from a large, unlabeled corpus using self-supervised objectives like masked language modeling or next-token prediction. This phase builds a foundational understanding of syntax, semantics, and world knowledge without task-specific labels, which can later be fine-tuned for downstream tasks.

Answer

To evaluate the model's performance on a benchmark dataset

Answer

To reduce the model's size for deployment on edge devices

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To adapt the model to a specific downstream task using labeled data

Question 10

A company uses Amazon SageMaker Pipelines for MLOps. The security team requires that all pipeline steps use only approved Docker images from a private Amazon ECR repository, and that all pipeline artifacts are encrypted with a customer managed KMS key. Which THREE steps must the company configure to meet these requirements? (Choose three.)

Accepted Answer

Specify a KMS key in the pipeline definition for encrypting output artifacts.. Amazon SageMaker Pipelines allows you to specify a KMS key in the pipeline definition to encrypt output artifacts at rest. This ensures that all artifacts generated by pipeline steps are encrypted using a customer managed KMS key, meeting the security team's encryption requirement.

Answer

Set an ECR lifecycle policy to delete untagged images older than 30 days.

Answer

Enable AWS Config rules to check for public ECR repositories.