AIP-C01 Multiple Select — 10 Questions

Question 1

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.)

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

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.

Answer

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

Answer

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

Answer

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

Question 2

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.

Answer

Email server logs.

Answer

Social media live streams.

Answer

Local hard drive files.

Question 3

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 4

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 5

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 6

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.

Answer

Verifying the S3 bucket encryption.

Answer

Measuring model training time.

Question 7

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.

Answer

Use a larger, more generic embedding model.

Answer

Increase the number of retrieved chunks to 1000.

Answer

Delete all indices and recreate them every hour.

Question 8

Which TWO metrics are natively available in Amazon CloudWatch for monitoring Bedrock model invocation performance?

Accepted Answer

InvocationLatency. Option B (InvocationLatency) is correct because Amazon Bedrock natively publishes the InvocationLatency metric to CloudWatch, which measures the time taken for a model invocation request to complete, directly reflecting model invocation performance. Option D (InvocationClientErrors) is correct because Bedrock also natively emits the InvocationClientErrors metric to CloudWatch, capturing the count of client-side errors (such as throttling or validation failures) during model invocations, which is essential for monitoring invocation health. Option A (VectorEmbeddingAccuracy) is not a CloudWatch metric provided by Bedrock; accuracy evaluation is handled through Bedrock model evaluation jobs, not runtime CloudWatch metrics. Option C (GPUUtilization) is not natively exposed by Bedrock in CloudWatch, since Bedrock is a fully managed service and does not surface underlying GPU infrastructure metrics to customers. Option E (ModelTrainingLoss) is not applicable because Bedrock does not expose training loss metrics in CloudWatch; Bedrock focuses on inference of foundation models rather than customer-visible training telemetry.

Answer

VectorEmbeddingAccuracy

Answer

GPUUtilization

Answer

ModelTrainingLoss

Question 9

Which TWO steps are required to properly secure Bedrock Knowledge Bases?

Accepted Answer

Use IAM roles for the Knowledge Base to access data. Enabling encryption and managing IAM access are the primary security configurations.

Answer

Disable all encryption to increase performance

Answer

Place the Knowledge Base in a public subnet

Answer

Grant public read access to the S3 bucket

Question 10

Which THREE factors influence the choice of embedding model for a RAG solution?

Accepted Answer

Supported languages. Language support, embedding dimensions, and maximum input token length are key factors.

Answer

Model's popularity

Answer

Model training cost