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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Exam Duration: | 180 minutes |
| Certificate Validity Period: | 3 years |
| Exam Price: | 300 USD |
| Passing Score: | 750 (scaled score 100โ1000) |
| Exam Format: | Multiple choice, Ordering, Multiple response, Matching |
| Available Languages: | Korean, English, Japanese, Simplified Chinese |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Recommended Training: | AWS Skill Builder - Official Training AWS Certified Generative AI Developer - Professional Exam Guide |
| Exam Registration: | AWS Certification Registration Pearson VUE Registration |
| Sample Questions: | Amazon AIP-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 2+ years of experience building production-grade applications on AWS or open-source technologies; 1+ year hands-on experience with generative AI implementation; knowledge of AWS compute, storage, networking, security, and deployment tools |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/examguides/ai-professional-01.html |
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NEW QUESTION # 91
A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.
The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.
Which combination of solutions will meet these requirements? (Select THREE.)
Answer: A,D,E
Explanation:
The correct combination is A, B, and D because these three options collectively satisfy the mandatory requirements for structured extraction, PII redaction before inference, regional human review, data residency, auditability, and high-scale availability with managed AWS services.
Option A is essential because Amazon Textract is the AWS-managed service designed to extract structured data from scanned documents such as forms, tables, and financial statements. Textract provides confidence scores, and Amazon Augmented AI (A2I) is purpose-built to route low-confidence extractions to human reviewers. Deploying Textract and A2I within the same Region ensures that the human review loop remains regionally constrained, meeting strict data residency requirements for applicants.
Option B satisfies the requirement to redact PII before inference by using AWS Lambda preprocessing. It also adds Amazon Bedrock guardrails to enforce safety controls on model outputs. Region-specific IAM roles ensure that only authorized principals in the correct Region can access the extracted data and invoke downstream services, strengthening residency enforcement and auditability.
Option D ensures that source documents are stored in Amazon S3 in the same Region as the applicant. Object metadata and tagging provide an auditable trail, supporting compliance reporting and traceability. S3 also provides the durability and availability needed to support 99.9% application availability as part of a well- architected pipeline.
Option C is not the correct approach for structured extraction from scans. Option E adds useful quality validation but is not strictly required to meet the stated requirements compared to A, B, and D. Option F is unrelated to the extraction/redaction/residency workflow requirements.
Therefore, A, B, and D are the best three choices to meet all stated requirements with minimal operational overhead.
NEW QUESTION # 92
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?
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 # 93
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?
Answer: D
Explanation:
Option C best satisfies the requirements because it combines application-aware observability, metric baselining, anomaly detection, and correlated alerting using fully managed AWS services with minimal operational overhead. Amazon CloudWatch Application Insights is designed to automatically monitor application health by analyzing metrics, logs, and events across EC2-based workloads. This aligns directly with the need to detect intermittent performance issues and deviations from expected behavior.
By publishing custom metrics using the CloudWatch embedded metric format, the application can track generative AI-specific signals such as recommendation quality indicators, token usage, request volume, and response latency from Amazon Bedrock foundation model calls. Adding dimensions such as request type or user segment enables fine-grained visibility into which workloads or customer groups are impacted when recommendation quality degrades.
A critical requirement is detecting degradation compared to established baselines and generating alerts within
10 minutes. CloudWatch anomaly detection automatically builds statistical models of normal behavior for time-series metrics and flags deviations without requiring manually tuned thresholds. This capability is well suited for monitoring foundation model behavior, which can vary subtly over time. When anomalies are detected, CloudWatch alarms can trigger notifications with contextual metric data quickly, meeting the alerting requirement.
CloudWatch Logs Insights complements the metric-based view by enabling log pattern analysis and correlation. Engineers can query application logs and model response logs to identify recurring error patterns or shifts in output behavior that explain why recommendations no longer align with user preferences.
Application Insights further correlates metrics and logs to surface probable root causes, reducing mean time to resolution.
The other options lack one or more critical elements. Option A focuses on infrastructure-level metrics without baseline anomaly detection. Option B emphasizes tracing and auditing but does not provide automated performance deviation analysis. Option D offers flexibility but requires significantly more development and operational effort than a native CloudWatch-based solution.
NEW QUESTION # 94
A university is building an AI-powered application that includes several sub-applications. The sub- applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: B,C
Explanation:
Amazon Bedrock Prompt Management is the purpose-built service for prompt lifecycle management. it provides native version control , allowing developers to test and compare variants side-by-side. Use of parameterized variables ensures that a single prompt structure can be consistently reused across different sub-applications (assistants vs. graders) while still being tailored to the specific context. To " integrate into the main application " with minimal overhead, Amazon Bedrock Flows provide a managed orchestration layer.
Flows allow developers to link managed prompts with AWS services (like knowledge bases or Lambda functions) without writing complex state-machine logic in Step Functions (Option B). This combination ensures consistent, auditable, and easily deployable prompt assets across the university ' s diverse use cases.
NEW QUESTION # 95
A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application ' s FMs follow regulatory compliance standards and maintain consistent formatting.
The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: B,C
Explanation:
Options A and C provide the most AWS-native governance approach. Amazon Bedrock Prompt Management supports reusable parameterized prompts and explicit prompt versions, allowing teams to preserve approved configurations and deploy stable versions instead of uncontrolled working drafts. An organizational approval gate can therefore be applied before a new prompt version becomes production-approved. AWS CloudTrail records Amazon Bedrock API activity, including the identity making the request, the operation performed, time, source information, and other event details, providing the required audit trail for Bedrock resource modifications and use. CloudWatch can complement this with operational usage monitoring and reporting.
Option B recreates prompt governance through S3, Lambda, and SNS. Option D uses general-purpose configuration storage rather than Bedrock-native prompt management. Option E adds substantial custom state and filtering logic without providing the same managed prompt-version lifecycle. AWS Documentation
NEW QUESTION # 96
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