AIP-C01 Practice Questions 2 — 50 Questions

Question 1

When preparing data for a Knowledge Base, why is it recommended to remove PII?

Accepted Answer

To comply with data privacy policies and reduce risk.. Removing PII ensures that the vector index does not store sensitive information, minimizing the risk of data leakage.

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To improve model inference speed.

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Because the models cannot process numbers.

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To reduce the file size.

Question 2

Which AWS tool enables you to test different models side-by-side using the same prompt?

Accepted Answer

Bedrock Playground. Amazon Bedrock Playground provides a dedicated interface where you can select multiple foundation models (e.g., Anthropic Claude, AI21 Labs Jurassic, Amazon Titan) and run the same prompt against them simultaneously, comparing outputs side-by-side. This is a built-in feature of the Bedrock console designed specifically for model evaluation and prompt experimentation. It eliminates the need to write code or manage infrastructure, allowing quick assessment of model performance, latency, and response quality.

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Amazon SageMaker Studio

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AWS Cloud9

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AWS Lambda Console

Question 3

What is the purpose of 'Foundation Models' (FMs) in the context of Amazon Bedrock?

Accepted Answer

To perform generative tasks like text completion or analysis.. Foundation Models (FMs) in Amazon Bedrock are large pre-trained models (e.g., Anthropic Claude, Amazon Titan, Meta Llama) that perform generative tasks such as text completion, summarization, question answering, and analysis. They are the core AI engines that Bedrock exposes via a unified API, allowing you to build generative applications without training models from scratch. The purpose of FMs is to provide these generative capabilities as a managed service.

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To provide a graphical user interface.

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To manage VPC networking.

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To store document metadata.

Question 4

You notice your SageMaker endpoint is underperforming. You identify that the model is CPU-bound during inference. Which action should you take?

Accepted Answer

Change to an instance type optimized for compute. If the model is CPU-bound during inference, the bottleneck is compute, not memory or instance count. Moving to a compute-optimized instance family (e.g., C-series) provides more vCPU per dollar and higher clock speeds, directly relieving the CPU saturation. This is the targeted fix for a CPU-bound workload.

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Reduce the batch size

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Increase memory limit in the container

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Increase the number of instances (horizontal scaling)

Question 5

Which THREE strategies should be implemented to ensure responsible AI practices in a production workload?

Accepted Answer

Perform regular model evaluation for bias. Option A is correct because regularly evaluating the model for bias (e.g., using fairness metrics such as demographic parity or equalized odds across subgroups) detects and mitigates discriminatory behavior before it harms users, which is a core responsible-AI control. Option C is correct because maintaining comprehensive logs of inputs, outputs, model versions, and decisions provides auditability and traceability, enabling incident investigation, compliance reporting, and accountability in production. Option D is correct because human-in-the-loop review for critical outputs ensures a qualified person validates high-impact decisions, catching harmful or erroneous model results that automation alone should not finalize. Option B does not belong because excluding all logging destroys auditability and incident forensics rather than protecting privacy; privacy should be handled via data minimization, encryption, and access controls. Option E does not belong because letting users train the model directly in production risks data poisoning, uncontrolled drift, and loss of governance over model behavior.

Answer

Exclude all logging to protect privacy

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Allow users to train the model directly

Question 6

An application uses Amazon Bedrock InvokeModel to generate text. You notice that the output length is consistently truncated. Which parameter should you adjust to ensure the full response is captured?

Accepted Answer

Increase the max_tokens parameter. In Amazon Bedrock InvokeModel, the max_tokens parameter (or its model-specific equivalent like max_gen_len) controls the maximum number of tokens the model can generate in the response. If output is consistently truncated, increasing max_tokens allows the model to produce a longer completion. The other parameters affect randomness or stopping criteria, not the length cap.

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Increase the temperature parameter

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Increase the top_p parameter

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Decrease the stop_sequences parameter

Question 7

A company requires that every AI-generated document must include a watermark for traceability. How can this be handled in Bedrock?

Accepted Answer

Use a custom post-processing Lambda function to inject metadata. Bedrock does not natively support watermarking of generated text, so the correct approach is to intercept the model response and apply a custom post-processing step — typically an AWS Lambda function — that injects a watermark, metadata tag, or traceable identifier into the output. This gives the company full control over the watermark format and ensures every generated document carries the required traceability marker.

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Use fine-tuning to force the model to watermark

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Enable watermarking in the Bedrock console settings

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Use AWS KMS to sign the generated text

Question 8

An application using Amazon Bedrock is experiencing high latency. You need to monitor the 'ModelInvocationLatency' metric to identify performance bottlenecks. Which AWS service provides the primary integration for this telemetry?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the primary monitoring service for AWS services including Amazon Bedrock. Bedrock automatically publishes metrics such as ModelInvocationLatency, InvocationCount, and InvocationThrottles to CloudWatch under the AWS/Bedrock namespace. You can create alarms, dashboards, and custom metrics from this telemetry to identify performance bottlenecks.

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AWS CloudTrail

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AWS X-Ray

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Amazon OpenSearch Service

Question 9

When using the Bedrock Converse API, how do you provide multiple documents to the model for consideration?

Accepted Answer

Include them in the 'document' field within the messages array. The Bedrock Converse API allows you to include documents directly in the messages array using the 'document' field. This enables the model to consider multiple documents in a single request without needing a Knowledge Base or S3 URLs.

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Upload them to S3 and include the URL in the system message

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They must be indexed in a Knowledge Base first

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Attach them as multi-part form data in the HTTP request

Question 10

You are using Amazon Bedrock and need to ensure your application respects regional service quotas. Which service allows you to request a quota increase?

Accepted Answer

AWS Service Quotas. AWS Service Quotas is the centralized service for managing and requesting increases for AWS service limits.

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AWS IAM

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Amazon CloudWatch

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AWS Support Center

Question 11

Which THREE metrics are best used for setting up auto-scaling policies on a SageMaker endpoint?

Accepted Answer

InvocationsPerInstance. InvocationsPerInstance, CPUUtilization, and GPUUtilization are the standard metrics to trigger scaling actions.

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IAMUserLoginCount

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S3RequestLatency

Question 12

Which THREE of the following are components of a robust AI governance framework in an AWS environment?

Accepted Answer

Implement Amazon Bedrock Guardrails. Option A is correct because Amazon Bedrock Guardrails provides configurable safeguards such as content filters, denied topics, and PII redaction that enforce responsible-AI policies at inference time, which is a core control of an AI governance framework. Option D is correct because Bedrock Model Invocation Logging captures invocation metadata (and optionally full request/response payloads) to CloudWatch Logs or Amazon S3, delivering the auditability and traceability that governance requires. Option E is correct because IAM roles with least-privilege access restrict who and what can invoke models or manage AI resources, satisfying the access-control and segregation-of-duties principles of governance. Option B does not belong because granting all users AdministratorAccess violates least privilege and destroys accountability. Option C does not belong because publicly hosting a model endpoint exposes it to unauthorized use and data exfiltration, directly undermining governance and security.

Answer

Provide all users with AdministratorAccess

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Publicly host the model endpoint

Question 13

A developer wants to ensure that the RAG pipeline is resilient to outages. What architectural pattern should be used?

Accepted Answer

Multi-region deployment with cross-region replication for the vector database.. A multi-region deployment with cross-region replication of the vector database ensures that if one AWS region becomes unavailable, the RAG pipeline can fail over to a replica region that already holds the embedded document vectors, preserving retrieval and generation. This directly addresses resilience to outages by removing the single-region failure domain for both compute and the vector store. Cross-region replication keeps the vector index synchronized so failover does not require re-ingesting and re-embedding the corpus.

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Single-node deployment.

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Hardcoding API endpoints.

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Using only one S3 bucket.

Question 14

Which THREE factors should you consider when tuning your inference endpoint for cost-efficiency?

Accepted Answer

Auto Scaling policies based on custom metrics. Instance type, auto-scaling configuration, and model optimization (like quantization) are critical levers for cost efficiency.

Answer

CloudTrail log retention period

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Number of S3 buckets used

Question 15

When implementing a RAG-based application, which TWO of the following ensure document security?

Accepted Answer

Apply metadata filters to control document access. Option A is correct because applying metadata filters at query time restricts retrieval to only the documents a given user is authorized to see, enforcing document-level access control within the RAG pipeline. Option B is correct because IAM-based connectors authenticate to data sources using identity and access management policies, ensuring that only permitted documents are ingested and that access respects existing enterprise permissions. Option C is wrong because a single shared user removes per-user accountability and grants everyone the same access, defeating least privilege. Option D is wrong because making all documents globally readable exposes sensitive content to every user and query. Option E is wrong because disabling indexing of sensitive documents doesn't secure them within RAG; it merely removes them from retrieval, which is a data-availability decision rather than an access-control mechanism.

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Use a single shared user for all data source connections

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Make all documents globally readable

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Disable indexing of sensitive documents

Question 16

Your organization needs to ensure that only authorized IAM users can access specific foundation models in Bedrock. Which configuration is required?

Accepted Answer

Attach an IAM policy with bedrock:InvokeModel allowing access to specific model ARNs. Amazon Bedrock integrates with AWS IAM to control access to foundation models. To restrict which IAM users can invoke specific models, you attach an IAM policy that includes the bedrock:InvokeModel action and specifies the ARNs of the allowed foundation models. This policy can be attached to users, groups, or roles, and it enforces least privilege at the model level. Without such a policy, users either have no access or overly broad access, so this is the required configuration.

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Enable AWS Organizations SCPs only

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Use Amazon Cognito user pools

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Use Resource-Based Policies on the foundation model

Question 17

Which THREE items must be configured when setting up a new Knowledge Base in Amazon Bedrock?

Accepted Answer

Embedding model selection.. Embedding model, data source, and vector database are the three mandatory components for a Knowledge Base.

Answer

Model evaluation dataset.

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External firewall rules.

Question 18

A company is building a RAG application on Amazon Bedrock. They need to store embeddings for a large-scale knowledge base with low-latency similarity search and full integration with LangChain. Which vector database should the developer recommend?

Accepted Answer

Amazon OpenSearch Serverless vector engine. Amazon OpenSearch Serverless vector engine is purpose-built for storing and querying high-dimensional embeddings with k-NN similarity search, offering low-latency retrieval at scale and native LangChain integration via the OpenSearch vector store class. It removes cluster management overhead while supporting the vector workloads RAG applications require.

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Amazon DynamoDB with global secondary indexes

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Amazon Aurora PostgreSQL without pgvector

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Amazon S3 with Athena query engine

Question 19

Which TWO of the following strategies are best for reducing the cost of SageMaker training jobs?

Accepted Answer

Using Managed Spot Training. Using Managed Spot Training and instance-appropriate types are standard methods for reducing training costs.

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Keeping training clusters running

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Increasing the number of instances

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Enabling detailed logging for every epoch

Question 20

A company is using RAG with Amazon Bedrock Knowledge Bases. They need to ensure that users can only retrieve documents they are authorized to see. How should this be implemented?

Accepted Answer

Use the filters attribute in the Retrieve API call based on document metadata. Amazon Bedrock Knowledge Bases support metadata filtering on the Retrieve and RetrieveAndGenerate APIs. By attaching authorization metadata (e.g., department, clearance level) to documents during ingestion and passing a filters attribute in the Retrieve call based on the user's identity, the application ensures users only retrieve documents they are authorized to see. This enforces document-level access control at query time.

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Encrypt the documents with different KMS keys

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Create separate Knowledge Bases for every user

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Apply IAM policies to the S3 bucket