AIP-C01 Practice Test 3 — 10 Questions

Question 1

When using the Bedrock Converse API, which object allows you to maintain context across multiple turns of a conversation?

Accepted Answer

messages. The Bedrock Converse API uses the `messages` array to hold the full conversation history, including alternating user and assistant turns. Each turn is represented as a message with a role and content, and by passing the entire array on subsequent calls, the model can see prior context. This is the standard mechanism for multi-turn conversations, as it explicitly maintains state across requests.

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inferenceConfig

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additionalModelRequestFields

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system

Question 2

Which Bedrock API parameter allows you to define a system-level role or persona for the model?

Accepted Answer

system. The 'system' parameter in the Bedrock Converse API (and related APIs) allows you to define a system-level instruction or persona for the model, such as 'You are a helpful customer service agent.' This system prompt is processed separately from user messages and sets the model's behavior, tone, and constraints for the entire conversation. It is the correct parameter for defining a system-level role or persona.

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messages

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top_p

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temperature

Question 3

What is the primary purpose of the 'max_tokens' parameter in text generation?

Accepted Answer

To set the maximum output length. The max_tokens parameter sets an upper bound on the number of tokens the model may generate in its response. It does not affect the model's reasoning or accuracy; it simply truncates output once the limit is reached, which is essential for controlling latency and cost.

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To increase response accuracy

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To improve model reasoning

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To filter out offensive language

Question 4

A developer needs to handle documents that are too large to fit in a single prompt. What is the most recommended approach?

Accepted Answer

Use a Knowledge Base to perform retrieval and generation.. Knowledge Bases for Amazon Bedrock implement a RAG (Retrieval-Augmented Generation) pattern: documents are chunked, embedded, and stored in a vector store, then only the most relevant chunks are retrieved and injected into the prompt at query time. This lets the model answer questions over documents of effectively unlimited size without exceeding the model's context window. It is the AWS-recommended, scalable approach for large-document Q&A.

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Compress the text using a ZIP algorithm before sending.

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Concatenate all text and send it in one prompt.

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Ask the user to split the document.

Question 5

When using the Bedrock Converse API, what is the best way to include dynamic variable values in a prompt?

Accepted Answer

Format the string in your application code. Use string formatting within your code to insert variables into the message text before sending the payload.

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Use the 'dynamic' parameter in the API call

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Use the 'template' field in the model config

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Pass values as a separate JSON array

Question 6

A team is evaluating different models on Amazon Bedrock. They want to compare the performance of Claude 3 Sonnet versus Llama 3 on a specific prompt set without writing custom scripts. Which tool should they use?

Accepted Answer

Amazon Bedrock Prompt Management and Playgrounds. Bedrock Prompt Management and Playgrounds provide a GUI to test and compare models directly.

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Amazon SageMaker Model Monitor

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Amazon QuickSight

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AWS Lambda functions

Question 7

A team wants to track who accessed a model and when. Which service provides this audit log?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records API activity in an AWS account, including who made a request, when, from which IP, and which resources were affected. For tracking who accessed a model and when, CloudTrail captures the InvokeModel API calls (and related Bedrock API actions) with the identity of the caller. This makes it the correct service for audit logging of model access.

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AWS Config

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Amazon GuardDuty

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Amazon CloudWatch

Question 8

Which THREE of the following are valid components of an Amazon Bedrock Agent test plan?

Accepted Answer

Validating the reasoning path of the agent.. Testing reasoning, tool invocation accuracy, and safety constraints are critical components of an agent test plan.

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Verifying the S3 bucket encryption.

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Measuring model training time.

Question 9

A developer is configuring a RAG system and wants to ensure the model has access to the most recent data. How should they handle updates?

Accepted Answer

Perform a 'Sync' operation on the Knowledge Base.. The 'Sync' operation in Knowledge Bases should be triggered whenever the source data changes in S3 to update the index.

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Wait for the Bedrock service to automatically detect changes.

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Use a cron job to restart the inference engine.

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Delete and recreate the knowledge base daily.

Question 10

Which TWO of the following are valid ways to improve the relevance of retrieved documents in a RAG system?

Accepted Answer

Implement metadata filtering to narrow the search space.. Metadata filtering and choosing an optimal chunking strategy are key levers to improve retrieval accuracy.

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Use a larger, more generic embedding model.

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Increase the number of retrieved chunks to 1000.

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Delete all indices and recreate them every hour.