AIF-C01 Practice Test 22 — 15 Questions

Question 1

A company is deploying an Amazon Lex chatbot that processes customer credit card information. The company must ensure that the chatbot does not log sensitive data and that the data is encrypted in transit. Which THREE actions should the company take? (Choose THREE.)

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

Accepted Answer

Ensure the Amazon Lex endpoint uses HTTPS for all API calls. Amazon Lex endpoints support HTTPS, which encrypts data in transit using TLS. By ensuring all API calls use HTTPS, the company protects sensitive credit card information from being intercepted during communication between the client and the Lex service.

Answer

Enable CloudTrail data events for the Lex bot to capture conversation logs

Answer

Enable S3 server access logs on the bucket storing the Lex bot configuration

Question 2

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:'.. 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 3

A company wants to evaluate the quality of a text generation model for a summarization task. They have reference summaries written by humans. Which automated metric compares the generated summary to the reference by measuring n-gram overlap?

Accepted Answer

ROUGE. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures n-gram overlap between generated and reference summaries. BLEU is for translation, BERTScore uses embeddings, and perplexity measures language model confidence.

Answer

BLEU

Answer

Perplexity

Answer

BERTScore

Question 4

A company uses Amazon Rekognition to analyze images for a user-generated content platform. To comply with data residency requirements, they must ensure that images are not processed outside a specific AWS region. What should the company do?

Accepted Answer

Use an AWS Lambda function to copy images to a bucket in the desired region and invoke Rekognition there.. It directly addresses the data residency requirement by using a Lambda function to copy images to an S3 bucket in the desired region and then invoking Rekognition in that same region. This ensures that image data never leaves the specified AWS region, as Rekognition processes images only in the region where the API call is made. The other options either do not prevent cross-region processing or are not applicable to Rekognition's data handling.

Answer

Enable Amazon Rekognition VPC endpoints.

Answer

Configure an AWS WAF rule to block cross-region processing.

Answer

Set up AWS Organizations to restrict the Rekognition service to one region.

Question 5

A company uses a generative AI model to create marketing copy. They want to ensure that customers know the content is AI-generated. Which practice directly addresses this transparency requirement?

Accepted Answer

Add a watermark or disclaimer stating 'This content was generated by AI'. Transparency in AI-generated content involves clearly disclosing to users that the content was produced by AI. This can be done through disclaimers, labeling, or other communication methods.

Answer

Implement AWS CloudTrail to log all model inference calls

Answer

Store all generated content in Amazon S3 with versioning enabled

Answer

Use a more powerful model to generate more natural-sounding text

Question 6

A healthcare company is deploying a model to predict patient readmission risk using Amazon SageMaker. The model processes protected health information (PHI). The company must adhere to HIPAA regulations, which require that all PHI is encrypted at rest and in transit, and that access to the data is logged and audited. The data is stored in an S3 bucket encrypted with SSE-S3. The SageMaker training job uses a custom Docker container that reads data from the S3 bucket and writes model artifacts back to another S3 bucket. The security team notices that the model artifacts in the output bucket are not encrypted. Also, the training logs in CloudWatch Logs contain PHI because the training script logs patient features. The company must fix these issues without delaying the deployment. What should the company do?

Accepted Answer

Modify the training container to avoid logging PHI, and configure the output S3 bucket to use SSE-KMS encryption.. It directly addresses both issues: modifying the training container to avoid logging PHI ensures CloudWatch Logs no longer contain protected health information, and configuring the output S3 bucket to use SSE-KMS encryption ensures model artifacts are encrypted at rest. SSE-KMS is preferred over SSE-S3 for HIPAA compliance because it provides additional controls like key rotation and audit trails. This approach fixes the problems without delaying deployment, as it requires only code and configuration changes.

Answer

Implement client-side encryption for the output bucket, and update the training script to mask PHI before logging.

Answer

Change the output S3 bucket to use SSE-KMS encryption, and enable server access logs for the bucket.

Answer

Configure the training job to use a VPC with private subnets, and enable data encryption in transit using TLS.

Question 7

A company is using Amazon Bedrock Knowledge Bases with a RAG pipeline. They want to improve the relevance of retrieved chunks for user queries. Which TWO configuration changes are likely to help?

Accepted Answer

Use a model with higher-dimensional embeddings. Reducing chunk size can improve precision, and using embeddings with higher dimensions may capture more semantic nuance. Increasing chunk size typically reduces precision, and disabling chunking would break retrieval. Increasing max context length does not affect retrieval.

Answer

Disable chunking and store entire documents as single vectors

Answer

Increase the model's max context length

Answer

Increase the chunk size

Question 8

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 9

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 10

A data scientist is using SageMaker Clarify to analyze a binary classification model for gender bias. The dataset has 80% male and 20% female applicants. The model predicts positive outcomes for 60% of males and 30% of females. Which fairness metric would directly capture this disparity in prediction rates?

Accepted Answer

Demographic parity. Demographic parity requires that the proportion of positive predictions is equal across groups. The difference between 60% and 30% directly measures violation of demographic parity.

Answer

Equalized odds

Answer

Disparate impact

Answer

Accuracy difference

Question 11

A healthcare company is using Amazon SageMaker to deploy a model that makes predictions on patient data. They need to ensure that the model's predictions are explainable to comply with regulations. Which approach should they take?

Accepted Answer

Use SageMaker Clarify to generate feature importance and explanations. SageMaker Clarify is specifically designed to provide model explainability, including feature importance and SHAP-based explanations, which are essential for regulatory compliance in healthcare. It helps stakeholders understand why a model made a particular prediction, addressing transparency requirements.

Answer

Use SageMaker Model Monitor to track predictions

Answer

Use SageMaker Experiments to log model parameters

Answer

Use SageMaker Debugger to analyze training gradients

Question 12

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Question 13

A company is implementing a Retrieval-Augmented Generation (RAG) pipeline with Amazon Bedrock Knowledge Bases. They need to store vector embeddings for their documents. Which vector store options are natively supported by Bedrock Knowledge Bases?

Accepted Answer

Amazon OpenSearch Serverless, Pinecone, MongoDB Atlas, and Amazon Aurora pgvector. Bedrock Knowledge Bases natively integrates with several vector stores, including Amazon OpenSearch Serverless, Pinecone, and MongoDB Atlas. Amazon Aurora pgvector is also supported but through Aurora's PostgreSQL compatibility. All four are valid options.

Answer

Only Pinecone and Amazon OpenSearch Serverless

Answer

Amazon DynamoDB and Amazon RDS for MySQL

Answer

Only Amazon OpenSearch Serverless

Question 14

A data scientist is building a RAG application using Amazon Bedrock Knowledge Bases. The team requires that responses only use information from the uploaded documents and reject queries that are not related to the documents. Which Bedrock feature should be used to enforce this?

Accepted Answer

Bedrock Guardrails. Bedrock Guardrails can deny topics outside the document scope. Knowledge Bases retrieves data but does not enforce content restrictions. Agents orchestrate actions but do not filter topics. Model evaluation is for testing, not runtime enforcement.

Answer

Bedrock Knowledge Bases

Answer

Bedrock Agents

Answer

Bedrock Model Evaluation

Question 15

A company wants to build a multi-language customer support chatbot using Amazon Bedrock. The chatbot should support English, Spanish, and French. The team needs to translate user queries into English before processing and then translate responses back. Which TWO approaches could achieve this? (Choose TWO)

Accepted Answer

Use a Bedrock Agent with an action group that calls a translation Lambda function. Bedrock Agents can orchestrate translation steps; prompt engineering can instruct the model to translate. Knowledge Bases and Guardrails are not translation solutions. Human agents are not automated.

Answer

Use a separate model fine-tuned for translation for each language pair

Answer

Configure Bedrock Guardrails to translate automatically

Answer

Store translated documents in a Bedrock Knowledge Base