support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Amazon
  4. AWS Certified Generative AI Developer - Professional AIP-C01

Free AWS Certified Generative AI Developer - Professional AIP-C01 Exam Braindumps
AWS Certified Generative AI Developer - Professional AIP-C01 Questions 25–32 (page: 5)

Updated On: 15-Apr-2026
Page 5 of 14
PDF with 97 Questions

A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.

The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.

Which solution will meet these requirements?

  1. Develop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to estimate token usage before sending requests to Amazon Bedrock. Configure the Lambda function to publish metrics to Amazon CloudWatch and trigger alarms when requests approach thresholds. Store detailed token usage in Amazon DynamoDB to report costs.
  2. Implement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected requests.
    Configure Amazon EventBridge rules to trigger notifications based on Amazon Bedrock Guardrails metrics.
    Use Amazon CloudWatch dashboards to visualize token usage trends across models.
  3. Deploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda function to analyze token-related failures. Use Amazon CloudWatch Logs Insights to generate reports on token usage patterns based on error logs from Amazon Bedrock API responses.
  4. Use Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request throttling based on custom usage plans with predefined token quotas. Configure API Gateway to reject requests that will exceed token limits.

Answer(s): A

Explanation:

Estimating token usage before invoking Amazon Bedrock requires model-specific tokenization logic so the application can proactively detect when an incoming request would approach or exceed each model's token limits. Publishing these estimates and actual usage to Amazon CloudWatch enables near real-time alerting at model-specific thresholds, and storing per-request token usage with business unit attribution in Amazon DynamoDB supports cost allocation at the required throughput scale (5,000+ requests per minute).



A retail company is developing a customer service application that must process 10,000 daily queries about products, orders, and warranties. The application must be able to respond to queries about 50,000 product documents that are updated every day. The application must integrate with an order management API to check the status of orders and to help process returns. The application must maintain context throughout multi-turn interactions with customers. The company must collect complete audit trails for application responses.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Deploy a fine-tuned Amazon Bedrock Anthropic Claude model for each product category. Create AWS Lambda functions to connect each model to the order management API. Store conversation history in Amazon DynamoDB.
  2. Create a custom model that uses continued pre-training on Amazon Bedrock to handle all product documentation. Set up an Amazon API Gateway REST API that uses AWS Lambda functions to connect the model to the order management API.
  3. Use Amazon SageMaker AI with containers to deploy models. Use Amazon Kendra to search product documents. Use AWS Step Functions to orchestrate calls to the order management API.
  4. Use an Amazon Bedrock agent with action groups to integrate with the order management API. Associate an Amazon Bedrock knowledge base with the agent to search product documentation by using Retrieval Augmentation Generation (RAG). Enable trace events to capture audit trails.

Answer(s): D

Explanation:

An Amazon Bedrock agent provides managed multi-turn conversation handling and maintains the interaction context while coordinating tool use. Action groups let the agent call the order management API for order status and returns without building a custom orchestration layer. Associating a Bedrock knowledge base enables managed RAG over the 50,000 frequently updated product documents, and trace events provide complete audit trails of the agent's reasoning steps, tool calls, and generated responses with minimal operational overhead.



An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs.

During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures.

A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Store intermediate outputs in Amazon DynamoDB. Pass only references between states. Create a Map state that retrieves the complete data from DynamoDB when required for each agent's processing step.
  2. Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameter for large outputs.
    Use the ResultPath field and the ResultSelector field to route S3 references between the agent steps while maintaining the sequential validation workflow.
  3. Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state. Configure each agent task to decompress the outputs before processing and to compress results before passing them to the next state.
  4. Configure a separate Step Functions state machine to handle each agent's processing. Use Amazon EventBridge to coordinate the execution flow between state machines. Use S3 references for the outputs as event data.

Answer(s): B

Explanation:

Passing Amazon S3 object references between Step Functions states avoids the 256 KB payload limit while keeping the existing sequential multi-agent ReAct workflow intact. Using Step Functions fields such as ResultPath and ResultSelector lets the workflow capture and forward only the S3 URIs and key metadata needed for observability, while the full intermediate traces and outputs remain stored in S3 for audit and debugging with minimal added orchestration.



A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgresQL database.

The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.

Which solution will meet these requirements with the LEAST development effort?

  1. Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, feature, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
  2. Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
  3. Keep the restaurant data in PostgresQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
  4. Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline.
    Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Answer(s): B

Explanation:

Amazon OpenSearch Service with vector (k-NN) search is a straightforward way to add semantic search at very large scale with low-latency queries, while keeping hourly data refreshes manageable through near-real- time indexing updates. Generating embeddings with Amazon Bedrock for both restaurant/review content and user queries enables complex natural-language intent matching without building extensive custom query parsing logic, minimizing development effort compared to implementing and tuning semantic retrieval inside PostgreSQL or building a custom knowledge base ingestion and serving path.



A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents.

The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions.

Which solution will meet these requirements?

  1. Create multiple prompt variants by using Prompt management in Amazon Bedrock. Manually test the prompts with simple clinical documents. Deploy the highest performing version by using the Amazon Bedrock console.
  2. Implement version control for prompts in a code repository with a test suite that contains complex clinical documents and quantifiable evaluation metrics. Use an automated testing framework to compare prompt versions and document performance patterns.
  3. Deploy each new prompt version to separate Amazon Bedrock API endpoints. Split production traffic between the endpoints. Configure Amazon CloudWatch to capture response metrics and user feedback for automatic version selection.
  4. Create a custom prompt evaluation flow in Amazon Bedrock Flows that applies the same clinical document inputs to different prompt variants. Use Amazon Comprehend Medical to analyze and score the factual accuracy of each version.

Answer(s): B

Explanation:

Version-controlling prompts in a repository preserves a complete historical record of prompt changes, and a test suite built from complex clinical documents with quantifiable evaluation metrics allows consistent comparison across versions to diagnose where inconsistencies arise. Automating the evaluation makes it easy to track performance patterns over time and prevents regressions when prompts change.



A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucination outputs when the company's model generates summaries of long technical documents. The model outputs include inaccurate or fabricated details. The company's current solution uses a large foundation model (FM) with a basic one-shot prompt that includes the full document in a single input.

The company needs a solution that will reduce hallucinations and meet factual accuracy goals. The solution must process more than 1,000 documents each hour and deliver summaries within 3 seconds for each document.

Which combination of solutions will meet these requirements? (Choose two.)

  1. Implement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.
  2. Use Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.
  3. Configure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.
  4. Increase the temperature parameter in Amazon Bedrock.
  5. Prompt the Amazon Bedrock model to summarize each full document in one pass.

Answer(s): B,C

Explanation:

Using a Bedrock knowledge base with RAG, semantic chunking, and tuned embeddings grounds each summary in retrieved source passages instead of forcing the model to reason over an entire long document in one prompt, which directly reduces hallucinations and helps meet factual accuracy targets while keeping latency low. Adding Bedrock guardrails as an output control layer can further reduce hallucination impact at scale by preventing known hallucination-like patterns from being returned to users, providing an additional safety backstop without adding heavy custom post-processing.



A company has a recommendation system. The system's applications run on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.

The system is experiencing intermittent issues. Some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of operational performance degradation compared to established baselines. The solution must also generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.

Which solution will meet these requirements?

  1. Configure Amazon CloudWatch Container Insights for the application infrastructure. Set up CloudWatch alarms for latency thresholds. Add custom metrics for token counts by using the CloudWatch embedded metric format. Create CloudWatch dashboards to visualize the data.
  2. Implement AWS X-Ray to trace requests through the application components. Enable CloudWatch Logs Insights for error pattern detection. Set up AWS CloudTrail to monitor all API calls to Amazon Bedrock.
    Create custom dashboards in Amazon QuickSight.
  3. Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.
  4. Use Amazon OpenSearch Service with the Observability plugin. Ingest model metrics and logs by using Amazon Kinesis. Create custom Piped Processing Language (PPL) queries to analyze model behavior patterns. Establish operational dashboards to visualize anomalies in real time.

Answer(s): C

Explanation:

CloudWatch Application Insights provides managed observability for application resources and helps detect performance issues against learned baselines with minimal setup. Publishing recommendation-quality and FM operational metrics (token usage, latency) as custom CloudWatch metrics with appropriate dimensions enables per-segment tracking, and CloudWatch anomaly detection can identify deviations from expected patterns and alarm quickly. CloudWatch Logs Insights adds near real-time log pattern analysis and correlation context so alerts can include supporting data within the 10-minute requirement.



An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs

when processing documents that exceed the FM's context window limits.

Which solution will resolve this problem?

  1. Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.
  2. Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.
  3. Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the Amazon Bedrock RetrieveAndGenerate API call to dynamically select the most relevant chunks based on embedding similarity scores.
  4. Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.

Answer(s): C

Explanation:

Using semantic chunking and the RetrieveAndGenerate API implements RAG so the model receives only the most relevant chunks for the user's task instead of the entire 50­200 page document, which avoids exceeding the context window and prevents truncation. Dynamic retrieval also improves consistency because each response is grounded in the same relevant source passages selected by embedding similarity rather than arbitrary fixed splits.



  PDF
Viewing page 5 of 14
Viewing questions 33 - 40 out of 97 questions


Post your Comments and Discuss Amazon AWS Certified Generative AI Developer - Professional AIP-C01 exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!