AIP-C01 Practice Test 2 — 10 Questions

Question 1

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.

Answer

The frequency of S3 bucket scans.

Question 2

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.

Answer

Chunking

Answer

Indexing

Answer

Parsing

Question 3

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.

Answer

AWS Secrets Manager

Answer

AWS Directory Service

Answer

Amazon Cognito

Question 4

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.

Answer

Increase the vector dimension size.

Answer

Use a larger model for embedding.

Answer

Reduce the chunk size.

Question 5

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

Answer

Increase the temperature

Answer

Change the model ID

Question 6

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

Answer

Amazon Bedrock Model Pre-training

Answer

Amazon Bedrock Auto-scaling

Question 7

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

Answer

Using only fixed-size overlap chunking

Question 8

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.

Answer

Use the Bedrock Import Vector API.

Answer

Import existing vectors directly into the OpenSearch index.

Question 9

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.

Answer

Amazon RDS

Answer

Amazon CloudWatch Logs

Answer

Amazon ElastiCache

Question 10

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.

Answer

Top_p

Answer

Max_tokens

Answer

Stop_sequences