AIP-C01 Practice Test 8 — 10 Questions

Question 1

When designing for compliance, which THREE actions are part of the 'Shared Responsibility Model' for an AWS customer?

Accepted Answer

Configuring encryption for the data at rest.. Data classification, encryption configuration, and IAM management are the customer's responsibility.

Answer

Maintaining the physical security of the data center.

Answer

Patching the underlying foundation model code.

Question 2

What is the primary benefit of deploying a JumpStart model on a Multi-Model Endpoint (MME)?

Accepted Answer

It optimizes resource utilization by hosting multiple models. A Multi-Model Endpoint hosts multiple models behind a single endpoint and dynamically loads/unloads them into shared container memory on demand. The primary benefit is resource utilization: you avoid provisioning a separate endpoint per model, so many low-traffic models share one instance's CPU/GPU and memory. This is the defining advantage of MME over single-model endpoints.

Answer

It improves inference latency for a single model

Answer

It simplifies the model training process

Answer

It provides higher availability than single-model endpoints

Question 3

A developer wants to choose a foundation model in Amazon Bedrock that offers the best balance of reasoning capability and latency for a customer support chatbot. Which tool should the developer use to evaluate models?

Accepted Answer

Bedrock Model Evaluation jobs.. The Bedrock Model Evaluation service allows developers to compare model performance using automated and human-in-the-loop metrics.

Answer

Amazon CloudWatch Metrics for Bedrock.

Answer

AWS Glue DataBrew.

Answer

Amazon SageMaker Model Monitor.

Question 4

A company is building a RAG application using Amazon Bedrock and needs to ensure that sensitive PII is filtered out before it reaches the foundation model. Which feature should be used to achieve this?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails allows users to configure content filters, including PII redaction, to prevent sensitive information from being processed by or returned from foundation models.

Answer

AWS WAF on the Bedrock API endpoint

Answer

Amazon Macie on the S3 bucket housing the source data

Answer

AWS Glue DataBrew

Question 5

A developer is troubleshooting an Amazon Bedrock Agent that is frequently hitting errors. Which TWO of the following steps should be taken to isolate the issue?

Accepted Answer

Check CloudWatch Logs for the associated Action Group Lambda functions.. Option C is correct because Bedrock Agents invoke Action Group Lambda functions to fulfill API operations, and their execution logs, errors, and timeouts are captured in CloudWatch Logs, making them the primary place to isolate backend failures. Option D is correct because the Agent's Trace output shows the step-by-step orchestration (pre-processing, orchestration, knowledge base/action group invocation, and post-processing), revealing exactly which stage or invocation failed. Option A is wrong because Bedrock is a fully managed service with no customer-accessible restart operation, and restarting would not diagnose the root cause. Option B is wrong because raising temperature to 1.0 only increases response randomness and would not isolate an error. Option E is wrong because base foundation models cannot be retrained by customers; customization is done via fine-tuning or continued pre-training, which is unrelated to troubleshooting.

Answer

Restart the Bedrock service.

Answer

Increase the model's temperature to 1.0.

Answer

Re-train the underlying base model.

Question 6

A healthcare company requires that all generative AI outputs be filtered for PII (Personally Identifiable Information) before being sent to the end user. Which Amazon Bedrock feature effectively manages this compliance requirement?

Accepted Answer

Amazon Bedrock Guardrails. Guardrails for Amazon Bedrock allows developers to configure PII filtering, profanity filters, and denial topics to ensure compliance.

Answer

AWS CloudTrail logs

Answer

AWS WAF on the API Gateway

Answer

IAM Policy evaluation for Bedrock

Question 7

A developer is creating a test suite for an LLM application. Which THREE of the following metrics are commonly used to evaluate RAG system performance?

Accepted Answer

Answer Relevance. Faithfulness, answer relevance, and context precision are the key components of RAGAS evaluation.

Answer

Model Training Loss

Answer

Inference Latency

Question 8

When using a Knowledge Base, what is the 'Base Prompt' used for?

Accepted Answer

To instruct the model on how to format the answer using retrieved context.. The base prompt serves as the template for the LLM to understand how to use retrieved context to answer the user's question.

Answer

To define the database schema.

Answer

To authenticate the user.

Answer

To store the conversation history.

Question 9

A company is concerned about model provider policy compliance. Where can they find the specific data processing terms for their chosen model?

Accepted Answer

In the AWS Service Terms and individual model provider documentation.. The AWS Service Terms and the Bedrock documentation page specify the data handling and privacy commitments for each model provider.

Answer

In the Amazon Bedrock console 'Model Evaluation' tab.

Answer

By asking the model during an inference session.

Answer

In the Amazon CloudWatch monitoring dashboard.

Question 10

An organization is concerned about 'Model Hallucinations' and needs a way to ground model outputs in trusted company documents. Which service supports this?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases implements Retrieval Augmented Generation (RAG) by connecting foundation models to trusted data sources stored in Amazon S3 or other supported repositories. It chunks, embeds, and indexes documents into a vector store, then retrieves relevant passages at inference time so the model's response is grounded in actual company content rather than parametric memory. This directly reduces hallucinations by supplying factual context in the prompt.

Answer

Amazon SageMaker Feature Store

Answer

AWS Glue Data Catalog

Answer

Amazon DynamoDB