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Amazon AIP-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.
Topic 2	Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 3	Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Topic 4	Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Topic 5	AI Safety, Security, and Governance: This domain addresses input output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.
The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.
Which solution will meet these requirements?

A. Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.
B. Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a
10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.
C. Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.
D. Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.

Answer: C

NEW QUESTION # 45
A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?

A. Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability.
B. Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution.
Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability.
C. Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.
D. Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation.

Answer: A

Explanation:
AWS Glue Data Catalog and its associated crawlers are the standard AWS tools for automatic discovery and centralized cataloging of datasets. For the generated summaries, storing them in Amazon S3 allows the use of object tags for metadata (like source IDs), making them easily queryable. The critical requirement for
" tamper-evident, immutable audit logs " is met by enabling Bedrock model invocation logging to an S3 bucket protected by S3 Object Lock (compliance mode). To further guarantee that logs have not been altered, AWS CloudTrail log file integrity validation uses cryptographic hashes to provide non-repudiation and a verifiable audit trail. This combination covers data management, metadata attribution, and high-standard security compliance.

NEW QUESTION # 46
A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.
Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.
Which combination of solutions will meet this requirement? (Select TWO.)

A. Switch to a multi-model endpoint. Use lazy loading without request batching.
B. Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.
C. Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.
D. Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.
E. Set the minimum number of instances to greater than 0. Enable response streaming.

Answer: D,E

NEW QUESTION # 47
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company ' s engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company ' s marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?

A. Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.
B. Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.
C. Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.
D. Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions.
Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.

Answer: A

Explanation:
Amazon CloudWatch provides the most integrated path for unifying technical and business metrics. By importing business metrics into CloudWatch (via custom metrics or metric streams), teams can build custom dashboards that provide a single pane of glass for both system health and conversion performance.
Composite alarms allow stakeholders to be notified only when multiple conditions are met (e.g., high latency and dropping conversion rates), reducing alert fatigue. Applying anomaly detection to these metrics is essential for GenAI workloads because performance baselines can shift subtly; CloudWatch can automatically detect these deviations and trigger alerts through Amazon SNS . This solution provides comprehensive correlation and automated alerting with less operational complexity than managing external visualization servers (Option B) or multi-service analytics pipelines (Option C).

NEW QUESTION # 48
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 PostgreSQL.
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?

A. 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.
B. 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.
C. 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.
D. 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, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.

Answer: A

Explanation:
Option B best satisfies the requirements while minimizing development effort by combining managed semantic search capabilities with fully managed foundation models. AWS Generative AI guidance describes semantic search as a vector-based retrieval pattern where both documents and user queries are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves results based on meaning rather than exact keywords.
Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving sub- second latency for the majority of queries. Because OpenSearch is a distributed, managed service, it automatically scales during peak traffic periods and provides cost-effective performance compared with building and tuning custom vector search pipelines on relational databases.
Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS manages the foundation models, eliminates the need for custom model hosting, and ensures consistency by using the same FM for both document embeddings and query embeddings. This aligns directly with AWS- recommended semantic search architectures and removes the need for model lifecycle management.
Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.
Option A does not meet the semantic search requirement because keyword-based search cannot reliably interpret complex natural language intent. Option C introduces scalability and performance risks by running large-scale vector similarity searches inside PostgreSQL, which increases operational complexity. Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented generation, not high- throughput semantic search.
Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and minimal development effort using AWS Generative AI services.

NEW QUESTION # 49
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