AIF-C01 Practice Test 27 — 15 Questions

Question 1

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

Accepted Answer

Configure a knowledge base with Retrieval Augmented Generation (RAG). Option B is correct because configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

Answer

Use prompt engineering to instruct the model to only use the knowledge base

Answer

Enable model invocation logging to review responses

Answer

Fine-tune the model on the product catalog data

Question 2

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

Answer

Amazon CloudFront

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Amazon DynamoDB

Answer

Amazon S3

Question 3

A data science team is evaluating foundation models for a code generation task. They need a model that is fine-tuned for code and can be deployed on Amazon SageMaker. Which THREE criteria are important to consider when selecting a model?

Accepted Answer

Licensing and usage terms. Option A is correct because licensing and usage terms directly impact whether a foundation model can be legally used for commercial code generation. Models like Code Llama or StarCoder have specific licenses (e.g., Llama 2 Community License, OpenRAIL-M) that may restrict fine-tuning, redistribution, or use in proprietary products. Ignoring these terms could lead to compliance violations when deploying on Amazon SageMaker.

Answer

Cost per token for inference

Answer

The training algorithm used

Question 4

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.. Option B is correct because 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 5

A company uses Amazon Comprehend to analyze customer sentiment. They discover the model performs poorly on text with slang from underrepresented groups. What is the most responsible action?

Accepted Answer

Collect more representative training data including slang. Option D is correct because the core principle of responsible AI requires that models be trained on data that is representative of the populations they serve. Amazon Comprehend's sentiment analysis is a supervised machine learning model; its poor performance on slang from underrepresented groups indicates a training data bias. Collecting more representative training data, including that slang, directly addresses the root cause by enabling the model to learn the linguistic patterns of those groups, improving fairness and accuracy without restricting access or masking the problem.

Answer

Restrict model use to only standard English

Answer

Remove slang from input before inference

Answer

Adjust the confidence threshold only for those groups

Question 6

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

Answer

Enable model invocation logging.

Answer

Configure a VPC endpoint.

Answer

Enable data encryption at rest.

Question 7

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

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

Answer

Any model in the account

Question 8

Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cause?

Accepted Answer

The prompt includes a stop sequence 'Assistant:'.. Option D is correct because the prompt includes the stop sequence 'Assistant:', which causes the model to halt generation as soon as it encounters that token sequence. In Claude v2, stop sequences are used to control the output length and structure; when the model generates the exact stop sequence, it truncates the response at that point, even if more content could have been produced.

Answer

The AWS CLI is missing the --endpoint-url parameter.

Answer

The max_tokens_to_sample is too low.

Answer

The model does not support this use case.

Question 9

A company is developing a chatbot using Amazon Bedrock and wants to ensure the model's responses do not include toxic or biased language. The company has a labeled dataset of undesirable responses. Which approach should be used to fine-tune the foundation model to reduce harmful outputs?

Accepted Answer

Use reinforcement learning from human feedback (RLHF) with a reward model trained on human preferences.. Reinforcement learning from human feedback (RLHF) is the correct approach because it directly optimizes the model to avoid toxic or biased outputs by training a reward model on human-labeled preferences. The reward model scores the model's responses, and the foundation model is fine-tuned via reinforcement learning to maximize these scores, effectively reducing harmful language. This method is specifically designed to align model behavior with nuanced human values, such as avoiding toxicity, which supervised fine-tuning alone cannot guarantee.

Answer

Perform supervised fine-tuning on a curated dataset of safe responses.

Answer

Use prompt engineering to instruct the model to avoid toxic language.

Answer

Implement adversarial validation by testing against toxic inputs.

Question 10

A developer is calling the Amazon Bedrock InvokeModel API to generate text with the AI21 Labs Jurassic-2 Mid model. The API call includes a maxTokens parameter, but the request fails with the error shown in the exhibit. What is the most likely cause of this error?

Accepted Answer

The parameter name is incorrect; the model expects 'maxTokens' with a capital T.. The AI21 Labs Jurassic-2 Mid model expects the parameter name 'maxTokens' with a capital 'T' (camelCase). The error occurs because the API request used a different casing (e.g., 'maxtokens' or 'max_tokens'), which the model's schema does not recognize. Amazon Bedrock's InvokeModel API passes parameters directly to the model, so parameter names must match the model's exact specification.

Answer

The API request is missing a required parameter such as 'prompt'.

Answer

The AWS region does not support the AI21 Labs model.

Answer

The value of 'maxTokens' exceeds the model's maximum limit.

Question 11

A healthcare organization is using Amazon Bedrock to analyze medical images and generate radiology reports. They need to comply with HIPAA regulations and ensure patient data is not used for model training. Which configuration should they use?

Accepted Answer

Use a provisioned throughput model with data isolation. Option B is correct because Provisioned Throughput with data isolation in Amazon Bedrock ensures that the customer's inference data (including patient medical images and reports) is not used for any model training or service improvement, and it provides a dedicated, isolated environment that meets HIPAA compliance requirements. This configuration guarantees that patient data remains within the customer's AWS account and is not shared with other customers or used to improve the base model.

Answer

Fine-tune the model using a custom dataset and deploy as a custom model

Answer

Use the on-demand model through Amazon Bedrock

Answer

Use a third-party model hosted outside of AWS

Question 12

A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?

Accepted Answer

Regularly audit the model for demographic bias. Option D is correct because regularly auditing the model for demographic bias is a core responsible AI practice, especially for identity verification systems where biased outcomes could lead to unfair treatment of certain customer groups. Amazon Rekognition's facial analysis and comparison features must be tested across diverse demographics to ensure equitable performance, as bias can arise from imbalanced training data or algorithmic artifacts.

Answer

Use only black-box models to protect intellectual property

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Increase model complexity to improve accuracy

Answer

Minimize the amount of training data collected

Question 13

A company uses an AI system to automate loan approvals. The model uses demographic features and achieves high accuracy, but the company wants to ensure compliance with responsible AI guidelines. Which practice best balances performance and fairness?

Accepted Answer

Remove sensitive attributes and monitor for proxy bias. Option C is correct because removing sensitive attributes (e.g., race, gender) from the training data directly addresses fairness by preventing the model from explicitly using these features. However, simply removing them is insufficient; monitoring for proxy bias (e.g., zip code or income correlating with race) is critical to ensure the model does not inadvertently learn discriminatory patterns through correlated features. This approach balances performance by retaining predictive power from non-sensitive features while actively auditing for fairness violations.

Answer

Use demographic features but with minimal monitoring

Answer

Use a complex black-box model and rely on post-hoc explanations

Answer

Optimize the model solely for accuracy on historical data

Question 14

A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?

Accepted Answer

Tune the model to achieve an acceptable balance between recall and precision. Option C is correct because responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.

Answer

Send all flagged transactions to customers for confirmation

Answer

Focus only on precision to minimize false positives

Answer

Increase the detection threshold to reduce false positives

Question 15

A financial services company is building a predictive model using Amazon SageMaker. The model training data contains personally identifiable information (PII). The company must ensure that the data is encrypted at rest and in transit, and that access to the data is logged. Which combination of AWS services meets these requirements?

Accepted Answer

Use S3 server-side encryption with AWS KMS (SSE-KMS), enable SageMaker inter-container traffic encryption, and enable CloudTrail data events for the S3 bucket. Option B is correct because it addresses all three requirements: SSE-KMS encrypts data at rest in S3, SageMaker inter-container traffic encryption ensures data in transit between training containers is encrypted, and CloudTrail data events for the S3 bucket log all access to the PII data. This combination provides a complete, auditable encryption and logging solution.

Answer

Use S3 server-side encryption with S3-managed keys (SSE-S3) and enable CloudTrail trail for S3 data events

Answer

Use S3 client-side encryption and configure SageMaker to use HTTPS for inter-container traffic

Answer

Enable S3 default encryption with AES-256 and use AWS CloudTrail for S3 data events