Free Amazon AIP-C01 Practice Test Questions

Question 1

A news aggregator uses a RAG architecture with Amazon OpenSearch Service (Provisioned) as the vector store. The system ingests breaking news in real-time, performing thousands of vector insertions per minute.Users are reporting that the "Related Articles" search is becoming increasingly slow (high latency) during these ingestion spikes. The OpenSearch cluster metrics show high CPU utilization and high JVM memory pressure.The index is configured with the standard settings for k-NN search.Which index configuration change should the developer apply to improve search performance under heavy ingestion load?

A : Change the vector data type from float32 to binary to reduce the storage footprint.

B : Switch the index engine from nmslib to faiss and enable the ivf (Inverted File) algorithm.

C : Enable the "Force Merge" operation to run automatically every minute to reduce the number of segments in the index.

D : Increase the refresh_interval setting of the OpenSearch index (e.g., from 1s to 30s or 60s).

Answer: D

Question 2

An online gaming platform is launching a GenAI-powered support assistant using multiple Foundation Models (FMs) available in Amazon Bedrock (including Amazon Titan and Anthropic Claude). The security team has issued strict requirements for the assistant's input handling:PII Protection: Users must be prevented from submitting email addresses or phone numbers.Toxicity: Hate speech and insults must be blocked immediately.Custom Blocking: A specific list of known "cheat codes" and "exploit keywords" must be blocked from entering the model context.Consistency: These rules must apply uniformly across all used FMs without rewriting application logic for each model.Which solution should you implement to meet these requirements with the LEAST operational overhead?

A : Develop a pre-processing AWS Lambda function using the Python re library to detect PII patterns and cheat codes. Use Amazon Comprehend DetectSentiment API to identify hate speech. Configure the application to call this Lambda function and verify the result before calling InvokeModel on Amazon Bedrock.

B : Implement a standardized system prompt across all FMs that instructs the models to refuse to answer if the input contains emails, phone numbers, hate speech, or words from the cheat code list. Use the Amazon Bedrock Converse API to ensure the system prompt is strictly enforced by the model’s reasoning engine.

C : Use AWS WAF (Web Application Firewall) attached to the application load balancer handling the chat traffic. Create custom regex rules to block PII patterns and cheat codes. Enable the AWS Managed Rules for Anonymous IP List to reduce toxicity risks from bad actors.

D : Create an Amazon Bedrock Guardrail. Configure PII filters to block emails and phone numbers, and enable Content filters for hate speech. Use the Word filters feature to upload the list of cheat codes and exploit keywords. Associate the Guardrail with the InvokeModel API calls for all used FMs.

Answer: D

Question 3

A healthcare company stores patient intake forms, insurance documents, and medical records as PDF files in multiple Amazon S3 buckets. Before building a GenAI document summarization system, the security team needs to identify which S3 buckets contain sensitive PII (like Social Security numbers, patient names, and medical record numbers) to ensure proper access controls are in place.Which AWS service should the security team use to automatically discover and classify sensitive data across their S3 buckets?

A : Amazon Inspector

B : Amazon Macie

C : Amazon Comprehend

D : AWS CloudTrail

Answer: B

Question 4

A financial services institution is building a proprietary 175-billion parameter Foundation Model (FM) to power a real-time fraud detection assistant. The project lifecycle involves two critical phases with distinct infrastructure requirements:Training Phase: The model must be pre-trained on petabytes of encrypted transaction logs. This requires synchronizing gradients across thousands of GPUs with minimal network latency to accelerate convergence.Deployment Phase: The fraud detection assistant must analyze transactions interactively with sub-millisecond latency. The traffic is highly variable, requiring the infrastructure to scale out automatically during market hours and scale in at night.Which combination of architectural strategies should the GenAI Developer implement to meet these requirements?

A : Deploy the model to Amazon SageMaker Asynchronous Inference. Configure the endpoint to buffer the incoming transactions in an internal queue, allowing the model to process them in large batches to maximize throughput.

B : Deploy the trained model to an Amazon SageMaker Real-time inference endpoint. Configure Target Tracking scaling policies to verify the SageMakerVariantInvocationsPerInstance metric and automatically adjust the number of instances (data parallelism) to handle the fluctuating traffic.

C : Launch the training job on Amazon EC2 UltraClusters (e.g., p4d instances) within Amazon SageMaker. Enable the SageMaker Distributed Data Parallel (SMDDP) library to maximize throughput and efficiently synchronize gradients across the high-speed Elastic Fabric Adapter (EFA) network.

D : Implement SageMaker Model Parallelism (SMP) on a fleet of Amazon EC2 Spot Instances for the inference deployment. Use the lowest-price allocation strategy to ensure the real-time endpoint remains cost-effective during peak market hours.

E : Use Amazon SageMaker Serverless Inference for the training phase. Configure the MaxConcurrency parameter to parallelize the data processing across thousands of ephemeral Lambda-based compute environments to reduce costs.

Answer: B,C

Question 5

A smart-home device manufacturer wants to use a Large Language Model (LLM) to identify usage trends from millions of daily device logs. Each log contains a specific device ID, the user's exact GPS coordinates, and a timestamped list of voice commands. Privacy regulations require that the manufacturer must not share granular location data or linkable user IDs with the analytics model, but the model still needs to understand regional usage patterns (e.g., "users in Northern Europe use heating more often").Which data pipeline architecture effectively prepares this data for the model while maintaining regulatory compliance?

A : Ingest the raw logs into an Amazon S3 bucket and use Amazon Athena to create a view that filters out columns containing Device IDs. Point the foundation model to the S3 bucket backing the Athena view and trust the model's system prompt to ignore any remaining location data.

B : Store the raw logs in Amazon S3 and enable S3 Object Lock in compliance mode to prevent modification. Use Amazon SageMaker Data Wrangler to feed the raw data directly into the model for fine-tuning, ensuring that the training job is run within a VPC without internet access.

C : Create an AWS Lambda function that hashes the Device ID and generalizes the GPS coordinates to a city or region level. Write these transformed logs to a separate "Analytics" S3 bucket and grant the foundation model access only to this secondary bucket for ingestion.

D : Implement a sidecar container in the ingestion pipeline that encrypts the GPS coordinates using asymmetric encryption. Pass the encrypted logs to the foundation model and provide the private key only within the RAG retrieval logic so the model can decrypt locations on-the-fly for regional analysis.

Answer: C

Free Amazon AIP-C01 Exam Practice Questions 2026