Free Amazon AIP-C01 Practice Exam Questions 2026

Question 1

A legal firm uses a Bedrock Knowledge Base connected to an S3 bucket containing case law documents and regulatory updates. Lawyers report that the AI assistant sometimes provides outdated legal precedents, even though the documents in S3 are updated daily by the research team.

The firm needs a solution that ensures the Knowledge Base always reflects the most current information with minimal manual intervention and the LEAST operational overhead.

Which solution addresses this issue MOST effectively?

A : Configure an S3 Event Notification to trigger a Lambda function whenever new documents are added or updated. The Lambda function calls the StartIngestionJob API to synchronize the Knowledge Base automatically. Set up CloudWatch alarms to monitor ingestion job failures.

B : Schedule an EventBridge rule to invoke the StartIngestionJob API every hour to keep the Knowledge Base synchronized with S3. Configure the ingestion job to process only modified files by enabling incremental sync. Use SNS to notify the team if ingestion fails.

C : Create a Step Functions workflow that runs daily to compare S3 object metadata with Knowledge Base ingestion timestamps. If discrepancies are detected, trigger the StartIngestionJob API. Use DynamoDB to track the last successful sync time for each document.

D : Enable S3 Versioning and configure the Knowledge Base to automatically detect changes in the S3 bucket. Set the synchronization mode to "Automatic" in the Knowledge Base settings so it continuously monitors S3 for updates without requiring manual triggers.

Answer: A

Question 2

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.

Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

Question 3

A financial analyst uses an internal GenAI tool to extract data from quarterly earnings PDFs into a standardized JSON format. The application is reporting frequent ValidationError exceptions in the backend. Upon investigation, the developer notices that for certain complex PDFs, the model either omits required fields or outputs the data as a Markdown table instead of JSON, despite the system prompt instructions.

Which troubleshooting and remediation workflow should the developer implement to resolve these prompt maintenance issues and prevent recurrence?

A : Enable model invocation logging to Amazon CloudWatch Logs to capture the raw inputs and outputs. Use CloudWatch Logs Insights to filter for the failed requests and identify the specific document patterns causing the prompt confusion. Create a test suite of these edge cases, iterate on the prompt template by adding "few-shot" examples of the correct JSON structure, and implement strict schema validation to detect format inconsistencies before processing.

B : Configure AWS X-Ray to trace the latency of the model invocations. Identify the requests that take longer than 5 seconds to generate a response, as these indicate prompt confusion. Switch the Foundation Model to a lower-latency version (e.g., from Claude to Titan) to ensure the model responds before the application times out.

C : Use Amazon SageMaker Model Monitor to track the statistical bias of the generated JSON output. Configure a baseline for the expected distribution of keys in the JSON object. Set up an alert to trigger if the drift in key frequency exceeds 10%, indicating a format inconsistency.

D : Implement an Amazon EventBridge rule to trigger an automatic retry with a higher temperature setting (e.g., 1.0) whenever the backend reports a ValidationError. This encourages the model to generate a different, more creative output format that might pass validation on the second attempt.

Answer: A

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 company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket. The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time. The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture. Which solution will meet these requirements?

A : Enhance embeddings by using a domain-adapted model that is specifically trained on emergency newscontent for improved vector similarity.

B : Migrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the scope ofresults retrieval.

C : Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3 objectmetadata.

D : Migrate to an Amazon Q Business index to perform structured metadata filtering and documentcategorization during retrieval.

Answer: C

Free Amazon AIP-C01 Practice Exams Questions 2026 - TheExamsLabs