AIP-C01 Practice Questions 1 — 50 Questions

Question 1

A company is using Amazon Q Business. How can they restrict access to specific data sources for different groups of users?

Accepted Answer

Configure ACLs in the data source connector. Amazon Q Business respects access control lists (ACLs) configured in the data source connector, so documents are only surfaced to users who have permission in the source system. When the connector indexes content, it ingests ACL information and enforces it at query time, ensuring each user sees only authorized data. This is the correct mechanism for restricting access to specific data sources per user group.

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Create separate Amazon Q applications

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Manually delete documents for specific users

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Apply IAM policies to the user's login

Question 2

You need to ensure that all data sent to an Amazon Bedrock model is encrypted in transit using private connectivity. What is the recommended service?

Accepted Answer

AWS PrivateLink (VPC Endpoints). AWS PrivateLink (VPC Endpoints) is the recommended service because it creates a private, one-way connection between a VPC and Amazon Bedrock using interface endpoints powered by ENIs, keeping traffic on the AWS backbone and off the public internet. This satisfies the requirement for private connectivity with encryption in transit (TLS) to the Bedrock endpoint. It avoids the need for internet gateways, NAT devices, or public IPs.

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AWS WAF

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AWS CloudFront

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AWS Certificate Manager

Question 3

Which SageMaker feature allows you to observe model performance drifts over time?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor automatically detects data drift, concept drift, and model quality issues.

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SageMaker Pipelines

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SageMaker Data Wrangler

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SageMaker Clarify

Question 4

When configuring an Amazon Bedrock Knowledge Base, which setting helps optimize the retrieval process for RAG applications?

Accepted Answer

Adjusting the chunk size and overlap. Selecting the appropriate embedding model and defining chunks size are key to optimizing retrieval quality and performance.

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Increasing the number of input tokens

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Deleting the data source

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Disabling encryption

Question 5

What is the primary function of the Bedrock 'Model Invocation Logs'?

Accepted Answer

To capture and store prompt/response payloads for auditing.. These logs are used for auditing and debugging by capturing the request and response payloads.

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To throttle incoming requests.

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To perform real-time model retraining.

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To reduce inference costs.

Question 6

Which service should be used to manage user access to Amazon Bedrock models?

Accepted Answer

AWS IAM.. AWS IAM (Identity and Access Management) is the service used to control access to AWS resources, including Bedrock APIs.

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Amazon Route 53.

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Amazon SageMaker.

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AWS Glue.

Question 7

A healthcare company is building a generative AI assistant on Amazon Bedrock that summarizes patient records. The compliance team requires that the assistant's outputs are traceable and that any harmful or biased content is detected before being shown to clinicians. The company wants to use AWS-native services and features. (Choose two.)

Accepted Answer

Enable model invocation logging in Amazon Bedrock to capture request and response metadata to Amazon CloudWatch Logs and Amazon S3.. Amazon Bedrock Guardrails evaluates prompts and completions against configurable policies to block harmful or biased content before it is presented, while model invocation logging captures detailed request and response data for traceability. Together they provide both preventive content moderation and a durable audit trail. AWS WAF, Amazon Macie, and AWS Trusted Advisor operate at different layers and cannot inspect or record natural language content for bias or harmful material.

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Use AWS Trusted Advisor to review the Bedrock model for ethical AI compliance.

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Enable Amazon Macie to automatically redact biased statements from Bedrock responses.

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Configure AWS WAF to inspect the generative AI responses for biased language.

Question 8

You are building a chat application and notice sensitive internal data is leaking in responses. You have enabled Guardrails. What else should be checked?

Accepted Answer

Review the system prompt and the Knowledge Base document retrieval filters. Guardrails filter model inputs and outputs, but they do not control what data enters the prompt in the first place. If sensitive internal data is leaking, the likely sources are the system prompt (which may contain secrets or instructions that surface data) or the Knowledge Base retrieval filters (which may be returning documents the user should not see). Reviewing both is the correct next step.

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Change the foundation model provider

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Increase the temperature setting

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Check if the model is fine-tuned on public data

Question 9

Which TWO of the following are supported as source data for Amazon Bedrock Knowledge Bases?

Accepted Answer

Atlassian Confluence.. S3 is the primary supported source, and Confluence is also natively supported as a data source.

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Email server logs.

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Social media live streams.

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Local hard drive files.

Question 10

You are implementing a multi-agent system. Which method is most effective for ensuring the agents do not loop infinitely when coordinating tasks?

Accepted Answer

Implementing a maximum iteration count. A maximum iteration count (or recursion/step limit) is the standard guardrail in agent orchestration frameworks like LangChain, LangGraph, and AWS Bedrock Agents to bound execution. When agents can call each other or themselves, a hard cap on turns prevents runaway loops that burn tokens and never terminate. Once the cap is hit, the orchestrator returns the best partial result or raises a controlled error.

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Caching agent responses

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Increasing the model temperature

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Using a smaller model

Question 11

Which THREE factors influence the cost of running an Amazon Bedrock Knowledge Base?

Accepted Answer

The amount of data stored in the vector database.. Option A is correct because an Amazon Bedrock Knowledge Base stores its embeddings in a vector store (for example, Amazon OpenSearch Serverless, Aurora pgvector, or Neptune Analytics), and the cost of that vector database scales with the volume of data/vectors stored. Option B is correct because the RetrieveAndGenerate API invokes a foundation model to generate the final answer, and you are billed for the input and output tokens the model consumes during that generation step. Option D is correct because ingesting documents into the knowledge base requires calling a Bedrock embedding model (such as Amazon Titan Text Embeddings), and you pay per token processed by that embedding model. Option C is not a Bedrock Knowledge Base cost factor because IAM users are free and concurrency of users does not drive Bedrock or vector-store charges. Option E is not correct because there is no per-scan charge for S3 buckets; S3 charges are based on storage, requests, and data transfer, not on how often the knowledge base syncs or scans the bucket.

Answer

The number of concurrent IAM users.

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The frequency of S3 bucket scans.

Question 12

You are designing a RAG pipeline. Which step in the ingestion process is responsible for converting unstructured text into a numerical representation?

Accepted Answer

Embedding. Embedding is the step that converts text chunks into dense numerical vectors (embeddings) that capture semantic meaning. These vectors are then stored in a vector database so that similarity search can retrieve relevant chunks at query time. Without embedding, the RAG pipeline cannot perform semantic retrieval.

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Chunking

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Indexing

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Parsing

Question 13

Which service should be used to provide secure identity and access management for an application accessing Bedrock?

Accepted Answer

AWS IAM. AWS IAM is the correct service for providing secure identity and access management for applications accessing Amazon Bedrock. IAM roles and policies define precisely which Bedrock actions (such as InvokeModel, RetrieveAndGenerate) an application or user can perform, and IAM roles can be assumed by compute resources like Lambda functions or EC2 instances to obtain temporary credentials. This is the foundational AWS access control mechanism that Bedrock integrates with natively for authorization.

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AWS Secrets Manager

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AWS Directory Service

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Amazon Cognito

Question 14

How can a developer ensure that the retrieval step in a RAG application focuses only on the most relevant documents when the knowledge base contains millions of files?

Accepted Answer

Implement metadata filtering in the query.. Metadata filtering (A) allows the retrieval step to narrow down the search space by applying filters on document attributes (e.g., date, category, author) before performing vector similarity search. This ensures that only documents matching the filter criteria are considered, improving relevance and reducing noise when the knowledge base is large.

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Increase the vector dimension size.

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Use a larger model for embedding.

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Reduce the chunk size.

Question 15

You are using a Bedrock Knowledge Base. You need to ensure that the search results are highly relevant to technical documentation. Which setting should you adjust?

Accepted Answer

Change the search type to HYBRID. Changing the search type to HYBRID in a Bedrock Knowledge Base combines semantic (vector) search with keyword-based (lexical) search, which significantly improves relevance for technical documentation containing specific terms, acronyms, error codes, and product names that pure semantic search may miss. Hybrid search merges results from both retrieval methods, capturing both conceptual similarity and exact keyword matches, making it ideal for technical content where precise terminology matters.

Answer

Update the S3 bucket policy

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Increase the temperature

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Change the model ID

Question 16

Which TWO features of Amazon Bedrock provide governance capabilities?

Accepted Answer

Amazon Bedrock Prompt Management. Amazon Bedrock Guardrails (E) is a governance feature because it lets you define and enforce content filters, denied topics, word filters, and sensitive-information (PII) redaction policies that are applied consistently across foundation models, which directly supports responsible-AI and compliance controls. Amazon Bedrock Prompt Management (C) provides governance by allowing prompts to be versioned, stored, and reused centrally, so teams can track changes, standardize approved prompts, and maintain an auditable lifecycle. The other options do not provide governance: API Gateway integration (A) is an architectural pattern for exposing Bedrock via REST APIs rather than a Bedrock governance capability, Model Pre-training (B) is not a Bedrock feature for customizing models (Bedrock offers fine-tuning and continued pre-training, not full pre-training), and Auto-scaling (D) is an operational capacity concern, not a governance control.

Answer

Amazon Bedrock API Gateway integration

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Amazon Bedrock Model Pre-training

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Amazon Bedrock Auto-scaling

Question 17

A developer is designing a RAG system and wants to ensure high-quality retrieval. Which THREE techniques should be implemented to improve semantic search relevance?

Accepted Answer

Using a reranking model to score retrieved documents. Option A is correct because a reranking model (e.g., a cross-encoder) re-scores the initially retrieved candidates against the query, capturing fine-grained query-document interactions that bi-encoder vector similarity misses, which directly boosts top-k relevance. Option B is correct because semantic chunking based on document structure (headings, sections, paragraphs) preserves coherent meaning units, so each embedding represents a complete idea rather than an arbitrary text slice, improving retrieval precision. Option D is correct because hybrid search combines lexical keyword matching (e.g., BM25) with dense vector search, capturing exact terms, rare entities, and acronyms that pure embeddings often miss while retaining semantic matching. Option C is not correct because simply maximizing vector dimensions increases storage and compute cost without guaranteeing better relevance, and can even hurt performance through overfitting or noise. Option E is not correct because fixed-size overlap chunking ignores semantic boundaries, frequently splitting related content and diluting embedding quality compared to structure-aware chunking.

Answer

Increasing the vector dimension size to maximum

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Using only fixed-size overlap chunking

Question 18

When migrating an on-premises RAG system to Amazon Bedrock, how should the team handle existing vector embeddings?

Accepted Answer

Re-embed the documents using a model supported by Amazon Bedrock.. When migrating a RAG system to Amazon Bedrock, existing vector embeddings from an on-premises system cannot be directly used because they were generated by a different embedding model. Embeddings are model-specific; dimensions and semantic spaces differ. Therefore, the documents must be re-embedded using an embedding model supported by Amazon Bedrock (e.g., Amazon Titan Embeddings or Cohere Embed). This ensures compatibility with Bedrock's vector store and retrieval.

Answer

Map the old embeddings to new dimensions using a linear function.

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Use the Bedrock Import Vector API.

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Import existing vectors directly into the OpenSearch index.

Question 19

You are using SageMaker JumpStart and need to enable 'Data Capture' for monitoring model inputs and outputs. Where is the captured data stored?

Accepted Answer

Amazon S3. When you enable Data Capture for a SageMaker endpoint (including JumpStart models), the captured data (inputs and outputs) is stored in an Amazon S3 bucket that you specify. This allows you to later analyze the data for monitoring, debugging, or retraining. CloudWatch Logs is used for logging but not for storing the captured data itself; the data capture feature writes to S3.

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Amazon RDS

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Amazon CloudWatch Logs

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Amazon ElastiCache

Question 20

Which parameter in Bedrock inference controls the deterministic nature of the output?

Accepted Answer

Temperature. Temperature is the Bedrock inference parameter that directly controls the randomness or determinism of the model's output. Setting temperature to 0 makes the model select the highest-probability token at each step, producing near-deterministic and repeatable responses. Higher values (up to 1) flatten the probability distribution, increasing creativity and variability.

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Top_p

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Max_tokens

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Stop_sequences