AIF-C01 Practice Exam 4 — 20 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.

Answer

Amazon Rekognition

Answer

Amazon Comprehend

Answer

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

Answer

SageMaker Experiments

Answer

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

Answer

Robustness

Answer

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

Answer

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.

Question 11

A company builds an AI system that generates medical diagnoses. To ensure patient safety and allow oversight, the company wants a human to review all high-risk predictions before they are acted upon. Which AWS service should they use?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is specifically designed to add human review workflows to machine learning predictions, including the ability to route high-risk or low-confidence predictions to human reviewers before action is taken. It integrates with Amazon SageMaker and other AWS services to create human review loops for sensitive use cases like medical diagnoses. This directly matches the requirement for human oversight of high-risk predictions.

Answer

AWS Lambda

Answer

Amazon SageMaker Ground Truth

Answer

AWS Step Functions

Question 12

Which TWO of the following are benefits of using Amazon Bedrock for building applications with foundation models?

Accepted Answer

No infrastructure management. Option A (No infrastructure management) is correct because Amazon Bedrock is a fully managed, serverless service: AWS handles provisioning, scaling, patching, and hosting of the underlying compute for the foundation models, so developers just call the API without managing any servers or clusters. Option C (Access to multiple foundation models) is correct because Bedrock provides a single unified API to choose from a range of foundation models from providers such as Anthropic, AI21 Labs, Cohere, Meta, Stability AI, and Amazon, letting you switch or compare models without integrating separate SDKs or endpoints. Option B is not a Bedrock benefit as stated, since fine-tuning is an optional capability you must explicitly configure (and not all models support it), not an automatic feature. Option D is incorrect because Bedrock is not free for all models; usage is billed per input/output token or per image, and pricing varies by model. Option E is incorrect because image generation is only available through specific models (e.g., Stability AI or Amazon Titan Image Generator), not as a universal built-in capability of the service.

Answer

Automatic model fine-tuning

Answer

Free tier for all models

Answer

Built-in image generation capability

Question 13

Refer to the exhibit. An IAM policy is attached to a user. Which models can the user invoke?

Accepted Answer

Only Claude v2. The IAM policy explicitly allows the `bedrock:InvokeModel` action only on the resource ARN `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`. This means the user can invoke only the Claude v2 model. No other models, including other Claude versions or any model with 'claude' in its name, are permitted because the resource ARN is exact and does not use wildcards.

Answer

No models

Answer

Claude v2 and any model with a name containing 'claude'

Answer

Any model in the account

Question 14

A media company must give an external analytics vendor temporary, auditable access to a curated S3 dataset used for fine-tuning a model. The vendor works from its own AWS account, and the company's security policy forbids sharing long-term IAM credentials. Which approach best meets the policy while keeping access auditable?

Accepted Answer

Configure a cross-account IAM role in the company account that the vendor assumes using AWS Security Token Service, with an external ID condition.. Cross-account IAM roles with AWS Security Token Service temporary credentials are the standard way to grant a partner access without exchanging secrets. The external ID prevents the confused deputy problem, and separate role assumption produces clean CloudTrail attribution. Presigned URLs, rotated IAM user keys, and CDN-signed access all either share secrets or obscure who actually accessed the data.

Answer

Generate a presigned URL for each S3 object and email the URLs to the vendor on a weekly schedule.

Answer

Enable S3 Block Public Access and place the dataset behind an Amazon CloudFront signed cookie distribution.

Answer

Create an IAM user in the company account for the vendor and rotate the access keys every 24 hours with a script.

Question 15

A company uses Amazon Bedrock with a custom model deployed via Amazon SageMaker. They want to monitor for data drift in input prompts over time. Which AWS service is best suited for this?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct choice because it is specifically designed to detect data drift in machine learning models, including input prompts for custom models deployed via SageMaker. It continuously monitors the distribution of input data against a baseline and alerts when drift occurs, which aligns with the requirement to monitor input prompts over time.

Answer

Amazon CloudWatch

Answer

AWS CloudTrail

Answer

Amazon Athena

Question 16

A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?

Accepted Answer

Use a RAG architecture with the company documentation as the knowledge base.. Retrieval-Augmented Generation (RAG) architecture retrieves relevant chunks from the company documentation at query time and injects them into the prompt, ensuring the model's response is grounded solely in the provided documents. This approach prevents the model from relying on its internal training data or generating information outside the documentation, which is critical for a closed-domain chatbot.

Answer

Use prompt engineering to instruct the model to only use documentation.

Answer

Fine-tune a foundation model on the company documentation.

Answer

Use a text classification model to filter responses.

Question 17

A company operates a customer service platform that uses Amazon Bedrock with a foundation model to generate automated responses. The system has been in production for three months. Recently, customers have reported that responses are becoming repetitive and less relevant over time. The development team notices that the model's performance has degraded, especially for queries about newer products that were added after the initial deployment. The team currently uses a static prompt with a fixed knowledge base that was set up at launch. The model is invoked via the Bedrock API with standard settings. The team wants to improve response quality without incurring high costs or extensive re-engineering. What should the team do?

Accepted Answer

Implement a feedback loop to periodically update the knowledge base with new product information and use a dynamic prompt that includes recent interactions.. The core issue is that the static knowledge base and prompt have become stale as new products were added. Implementing a feedback loop to periodically update the knowledge base with new product information and using a dynamic prompt that includes recent interactions directly addresses the root cause of degradation—outdated context—without requiring costly fine-tuning or model swaps. This approach leverages Amazon Bedrock's native capabilities for retrieval-augmented generation (RAG) to keep responses relevant and non-repetitive.

Answer

Increase the temperature parameter to 0.9 to introduce more randomness and reduce repetition.

Answer

Fine-tune the model every week on the latest customer interactions using Amazon SageMaker.

Answer

Switch to a larger foundation model to handle the increased complexity of new products.

Question 18

A company is using Amazon Comprehend to analyze customer feedback. They need to ensure that the documents are encrypted at rest. What should they do?

Accepted Answer

No action is needed; Amazon Comprehend automatically encrypts data at rest using AES-256. Amazon Comprehend automatically encrypts all data at rest using AES-256 encryption by default, with no additional configuration required. This encryption covers both the documents processed by the service and any models or artifacts stored internally. Therefore, no action is needed from the customer to enable encryption at rest.

Answer

Enable encryption using AWS KMS in the Comprehend console

Answer

Store documents in an encrypted S3 bucket and use a VPC endpoint

Answer

Use SSL/TLS for all API calls to Comprehend

Question 19

During a security review, it is found that an Amazon SageMaker notebook instance has outbound internet access, which could lead to data exfiltration. The notebook must only access resources within the VPC. Which step should be taken to restrict internet access?

Accepted Answer

Configure the notebook instance in a VPC with no internet gateway or NAT device, and set the notebook's 'Direct Internet Access' option to 'Disabled'. Disabling 'Direct Internet Access' on a SageMaker notebook instance and placing it in a VPC without an internet gateway or NAT device ensures the notebook cannot reach the public internet. This configuration forces all traffic to stay within the VPC, preventing data exfiltration via outbound internet connections while still allowing access to VPC resources.

Answer

Modify the notebook instance's IAM role to deny s3:GetObject

Answer

Attach a security group that denies all outbound traffic to 0.0.0.0/0

Answer

Disable the SageMaker notebook instance's root volume encryption

Question 20

A financial services company uses Amazon Bedrock to power a customer-facing chatbot that provides investment advice. The company must ensure that the chatbot's responses comply with regulatory standards, meaning that the model should not generate advice that is speculative or promises returns. The company has implemented Bedrock Guardrails with content filters. However, during testing, the chatbot still generates responses that violate the guidelines. A review of the guardrail configuration shows that the content filters are set to the lowest sensitivity. The company wants to enforce stricter filtering without completely blocking legitimate responses. What should the company do?

Accepted Answer

Increase the sensitivity of the content filters in the Bedrock Guardrails configuration.. Increasing the sensitivity of the content filters in Bedrock Guardrails directly addresses the issue: the current filters are set to the lowest sensitivity, allowing speculative or promise-based responses to pass through. By raising the sensitivity, the guardrails will block more non-compliant content while still permitting legitimate investment advice, striking the required balance between regulatory compliance and functionality.

Answer

Use a different foundational model that has built-in compliance filters.

Answer

Configure the chatbot to route all responses to a human reviewer before delivering to the customer.

Answer

Add a deny topic for investment advice to completely block that topic.