AIP-C01 Practice Questions
AWS Certified Generative AI Developer - Professional
Last Update 15 hours ago
Total Questions : 119
Dive into our fully updated and stable AIP-C01 practice test platform, featuring all the latest AWS Certified Professional exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.
Our free AWS Certified Professional practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about AIP-C01. Use this test to pinpoint which areas you need to focus your study on.
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?
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?
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?
A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company ' s book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers ' and editors ' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?
A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates.
The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents.
Which solution will meet this requirement with the LEAST operational overhead?
