AIP-C01試験ガイドを購入すると、購入したテストバンクをすぐにダウンロードできます。 AIP-C01試験の教材のすべての内容を把握するだけで十分であり、AIP-C01試験問題の合格率は非常に高いため、AIP-C01試験の学習と準備に必要な時間は20〜30時間です。そして約98％-100％。It-Passports最新のAIP-C01クイズトレントには3つのバージョンがあり、学習に最適なものを選択できます。全体として、AIP-C01クイズ準備には多くのメリットがあります。
| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Generative AI Developer - Professional |
| Exam Number: | AIP-C01 |
| Available Languages: | English, Korean, Japanese, Simplified Chinese |
| Exam Format: | Multiple response, Multiple choice, Matching, Ordering |
| Passing Score: | 750 (scaled score 100–1000) |
| Real Exam Qty: | 75 (65 scored + 10 unscored) |
| Exam Price: | 300 USD |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 180 minutes |
| Related Certifications: | AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner |
| 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 |
ほとんどの人は時間を節約するために速達を使用する傾向があるため、AIP-C01準備試験は購入後5〜10分以内に送信されます。プラットフォームで料金を支払う限り、指定された時間内に関連する試験資料をメールボックスに配信します。当社はサービス全体を非常に重視しており、AIP-C01試験資料の配信に問題がある場合：AWS Certified Generative AI Developer - Professional、お知らせください。メッセージまたは電子メールを利用できます。
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質問 # 55
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?
正解：D
解説：
Option B best meets the requirements because it provides systematic diagnosis, measurable comparison, and historical traceability of prompt performance. By placing prompts under version control and testing them against complex clinical documents, the company can consistently reproduce issues, track regressions, and compare prompt behavior using quantifiable metrics such as factual accuracy, completeness, and consistency.
Automated testing ensures scalability and repeatability, while version history preserves prompt evolution over time.
Option A lacks objective metrics and does not address complex documents. Option C focuses on live traffic experimentation but does not inherently diagnose prompt inconsistencies or preserve detailed historical evaluations. Option D adds medical entity analysis but introduces unnecessary service coupling and does not provide robust prompt version history or automated comparative benchmarking. Therefore, Option B is the most complete and disciplined solution.
質問 # 56
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
正解：B
解説：
Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.
AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses.
Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.
Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely.
Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.
Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.
Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.
質問 # 57
An ecommerce company is building an internal platform to develop generative AI applications by using Amazon Bedrock foundation models (FMs). Developers need to select models based on evaluations that are aligned to ecommerce use cases. The platform must display accuracy metrics for text generation and summarization in dashboards. The company has custom ecommerce datasets to use as standardized evaluation inputs.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)
正解：B、C
解説：
The least operational overhead approach is to use managed Amazon Bedrock model evaluation workflows with datasets stored in Amazon S3, and then publish results into Amazon CloudWatch for dashboards. That is exactly what options B and C combine.
Step B correctly places standardized evaluation inputs in Amazon S3 and focuses on granting the evaluation workflow the right permissions to read those datasets. In practice, the key requirement is controlled access to the S3 objects used as evaluation datasets. Establishing IAM permissions and private access patterns (such as using VPC connectivity patterns where applicable to the organization's networking posture) is aligned with enterprise requirements and avoids building custom storage or data distribution systems for evaluators.
Step C then operationalizes the evaluation lifecycle with minimal infrastructure: a scheduled AWS Lambda function starts evaluation jobs using the S3 dataset location, and a second Lambda function checks job status and pushes results and operational signals to CloudWatch. This meets the platform requirement to surface accuracy metrics in dashboards because CloudWatch metrics/logs can be visualized in dashboards and queried through CloudWatch Logs Insights. It also supports continuous, standardized comparisons across models without requiring developers to run ad-hoc experiments.
The alternatives introduce more operational burden. D and E rely on Amazon SageMaker-based tooling, notebook jobs, and open source evaluation frameworks, which require more environment management, dependency control, scaling considerations, and maintenance over time. A includes CORS, which is primarily a browser-access concern and does not address how Bedrock-managed evaluation jobs securely access S3 in the typical service-to-service pattern.
Therefore, B + C achieves standardized model evaluation, automated scheduling, and dashboard-ready observability with the smallest operations footprint.
質問 # 58
A healthcare company is using Amazon Bedrock to build a GenAI application to analyze patient feedback data from CSV files, JSON documents, and text files. The company needs to make the data available for a RAG solution that requires high data quality to prevent hallucinations. The GenAI application will use the data to make accurate clinical recommendations. The application must be highly scalable to handle data in near real time. Data attrition is also high.
Before the company feeds data to a foundation model (FM), the company needs to validate data completeness, detect anomalies, remove personally identifiable information (PII), and monitor quality metrics. The application must be serverless, provide automated rule recommendations, and generate quality scores for regulatory compliance.
Which solution will meet these requirements?
正解：B
解説：
AWS Glue Data Quality is the correct core service because it directly addresses the distinctive requirements that separate this question from ordinary ETL validation. AWS describes Glue Data Quality as a managed, serverless capability for measuring and monitoring dataset quality. It uses Data Quality Definition Language (DQDL) for rules, can automatically analyze datasets and recommend rules, generates a Data Quality score after rule evaluation, supports machine-learning-based anomaly detection, and integrates with Amazon CloudWatch.
Completeness can be evaluated through DQDL rules such as IsComplete . AWS rule recommendations can automatically generate completeness, uniqueness, value-domain, and column-length rules based on observed data. Anomaly detection learns historical statistics and identifies values outside predicted bounds; AWS can additionally recommend future data-quality rules based on detected anomalies.
Glue Data Quality can publish pass/fail metrics into the Glue Data Quality CloudWatch namespace, making the results usable for monitoring, alarms, and regulatory-quality dashboards.
There is one important technical issue in the wording of option B: AWS Glue Data Quality itself does not remove PII. The complete AWS Glue implementation would pair Glue Data Quality with the AWS Glue Detect PII transform. AWS documents that this transform can identify sensitive entities and then mask, redact, hash, or otherwise process identified PII. Therefore, B is clearly the intended and strongest listed answer, but a production implementation should add the Glue PII transform to fully satisfy the question ' s PII-removal requirement.
A requires extensive custom validation. C does not provide the requested automated rule recommendations and managed quality-score capabilities. D handles PII detection but lacks automated DQDL recommendations, ML anomaly detection, and native data-quality scoring.
質問 # 59
A company wants to select a new FM for its AI assistant. A GenAI developer needs to generate evaluation reports to help a data scientist assess the quality and safety of various foundation models FMs. The data scientist provides the GenAI developer with sample prompts for evaluation. The GenAI developer wants to use Amazon Bedrock to automate report generation and evaluation.
Which solution will meet this requirement?
正解：B
解説：
Option B is correct because it uses the managed evaluation capability in Amazon Bedrock that is intended specifically for comparing foundation models using a consistent prompt set and producing structured results with minimal custom tooling. In a Bedrock evaluation workflow, you provide an input dataset of prompts, typically in JSON Lines format so each line represents one evaluation record. Storing the JSONL file in Amazon S3 allows Bedrock to read the dataset at scale and write standardized evaluation outputs back to S3 for downstream analysis, sharing, and retention.
The key requirement is to assess both quality and safety across multiple models. A Bedrock evaluation job can use a judge model to score the generated outputs against defined criteria. This approach supports repeatable, apples-to-apples comparisons because the same judge model and scoring rubric can be applied to every candidate foundation model. The candidate models are configured as generators, meaning each evaluation job run uses one selected FM to produce answers for the same prompt set, and the judge model evaluates those answers. That matches the requirement to generate evaluation reports that help a data scientist select the best FM.
Option A does not use Bedrock evaluation jobs, and a knowledge base plus RetrieveAndGenerate is a RAG pattern, not an evaluation framework. It would produce responses but not standardized scoring and reporting suitable for model selection. Option C is incorrect because Bedrock evaluation outputs are delivered to S3, not directly to a BI destination, and selecting the candidate FM as the evaluator conflicts with the intended pattern of using a stable judge model. Option D misuses knowledge bases and retrieval evaluation types when the requirement is prompt-based model assessment rather than evaluating retrieval quality.
質問 # 60
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AIP-C01合格問題: https://www.it-passports.com/AIP-C01.html