AIF-C01 Practice Test 8 — 10 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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.

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AWS Lambda

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Amazon SageMaker Ground Truth

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AWS Step Functions

Question 2

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.

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Automatic model fine-tuning

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Free tier for all models

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Built-in image generation capability

Question 3

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.

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No models

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

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Any model in the account

Question 4

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.

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

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

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

Question 5

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.

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Amazon CloudWatch

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AWS CloudTrail

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Amazon Athena

Question 6

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.

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

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Fine-tune a foundation model on the company documentation.

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Use a text classification model to filter responses.

Question 7

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.

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

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

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

Question 8

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.

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Enable encryption using AWS KMS in the Comprehend console

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

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Use SSL/TLS for all API calls to Comprehend

Question 9

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.

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

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

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Disable the SageMaker notebook instance's root volume encryption

Question 10

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.

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

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

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