100% Free Amazon AIP-C01 Practice Test Questions

Question 1

A travel company is building a complex "Vacation Planner" system using Amazon Bedrock Agents multi-agent collaboration:Flight Specialist Agent: Searches flight databases using a Lambda-based action groupHotel Specialist Agent: Searches hotel inventory using an API-based action groupSupervisor Agent: Interacts with the user, breaks down the "Plan a trip to Paris" request, delegates sub-tasks to the specialist agents, and aggregates their findings into a final itineraryThe developer needs to optimize the system to reduce latency for simple, single-domain queries (like "Find me a hotel in Paris") while maintaining the ability to handle complex multi-step requests (like "Plan a complete 5-day vacation to Paris").Which collaboration mode configuration meets this requirement?

A : Configure the supervisor agent in Supervisor with routing mode. Simple queries are routed directly to the appropriate collaborator agent (Flight or Hotel Specialist), while complex queries automatically trigger full supervisor mode for orchestration across multiple agents.

B : Configure the supervisor agent in Supervisor mode. The supervisor always breaks down the request, invokes collaborator agents in parallel, and synthesizes the results regardless of query complexity.

C : Deploy all three agents as independent agents without multi-agent collaboration. Use Amazon Lex to parse user intent and route to the appropriate single agent, then use a Lambda function to aggregate results from multiple agent invocations.

D : Use AWS Step Functions to create a Choice state that evaluates query complexity. Route simple queries to individual agents via direct Lambda invocation, and complex queries to the Bedrock supervisor agent.

Answer: A

Question 2

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 3

A financial services firm processes thousands of unstructured PDF earnings reports daily. The engineering team uses Anthropic Claude 4.5 Sonnet on Amazon Bedrock to extract specific Key Performance Indicators (KPIs) and populate a downstream SQL database.Users have reported that while the model handles narrative text well, it consistently hallucinates values when processing financial tables that span multiple pages or contain complex nested headers. The raw text ingested from the OCR pipeline often arrives with broken formatting, missing whitespace, and merged columns.Which strategy should the GenAI Developer implement to enhance the input data quality and resolve the extraction errors?

A : Use an AWS Lambda function to apply a uniform truncation to all input documents, limiting them to the first 2,000 tokens to ensure the model focuses only on the high-level summary tables usually found at the beginning of reports.

B : Increase the temperature parameter of the Claude 4.5 Sonnet model to 0.7 to encourage the model to be more creative in interpreting the broken table structures.

C : Introduce a pre-processing step using the Amazon Bedrock ConverseAPI with a lightweight model like Anthropic Claude 3.5 Haiku. Prompt this model to rewrite the raw OCR output into valid Markdown tables, then pass this cleaned, structured text to Claude 4.5 Sonnet for the final KPI extraction.

D : Replace the OCR pipeline with Amazon Textract's raw text detection. Directly concatenate the raw LINE block outputs into a JSON object and feed this massive JSON structure into Claude 4.5 Sonnet without any further serialization.

Answer: C

Question 4

A legal technology company is building a generative AI application to draft contract clauses. The application must utilize a repository of 200,000 proprietary legal documents that is updated with new case files every night.The Chief Legal Officer has set the following strict requirements:Attribution: Every generated clause must include a direct citation to the specific case file it was derived from.Freshness: The model must incorporate data from documents added to the repository within the last 24 hours.Accuracy: The solution must minimize hallucinations and strictly adhere to the facts present in the proprietary documents.Which architectural strategy should the GenAI engineer implement to meet these requirements?

A : Use Amazon Bedrock Custom Models to fine-tune a base Large Language Model (LLM) on the proprietary documents. Configure an automated pipeline using Amazon SageMaker Pipelines to re-train and deploy a new model version every night.

B : Perform Continued Pre-training on an Amazon Titan model using the proprietary documents to adapt the model to the legal domain. Use the resulting custom model to generate clauses based on its internal knowledge of the dataset.

C : Use Amazon SageMaker JumpStart to deploy an open-source model to a real-time inference endpoint. Implement a LangChain workflow that loads the entire document dataset into the model's context window for each inference request.

D : Implement a Retrieval Augmented Generation (RAG) architecture using Amazon Bedrock Knowledge Bases. Configure the data source to sync with the document repository daily and set the generation configuration to return source citations.

Answer: D

Question 5

A legal technology company is building a generative AI application to draft contract clauses. The application must utilize a repository of 200,000 proprietary legal documents that is updated with new case files every night.The Chief Legal Officer has set the following strict requirements:Attribution: Every generated clause must include a direct citation to the specific case file it was derived from.Freshness: The model must incorporate data from documents added to the repository within the last 24 hours.Accuracy: The solution must minimize hallucinations and strictly adhere to the facts present in the proprietary documents.Which architectural strategy should the GenAI engineer implement to meet these requirements?

A : Use Amazon Bedrock Custom Models to fine-tune a base Large Language Model (LLM) on the proprietary documents. Configure an automated pipeline using Amazon SageMaker Pipelines to re-train and deploy a new model version every night.

B : Perform Continued Pre-training on an Amazon Titan model using the proprietary documents to adapt the model to the legal domain. Use the resulting custom model to generate clauses based on its internal knowledge of the dataset.

C : Use Amazon SageMaker JumpStart to deploy an open-source model to a real-time inference endpoint. Implement a LangChain workflow that loads the entire document dataset into the model's context window for each inference request.

D : Implement a Retrieval Augmented Generation (RAG) architecture using Amazon Bedrock Knowledge Bases. Configure the data source to sync with the document repository daily and set the generation configuration to return source citations.

Answer: D

Free Practice Amazon AIP-C01 Exam Questions 2026