Amazon
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AWS Certified Generative AI Developer - Professional
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Version: 6.4
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Questions & Answers DEMO PDF
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, Question 1. (Single Select)
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.
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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
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solution must also scale cost-effectively during peak usage periods.
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Which solution will meet these requirements with the LEAST development effort?
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A: Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules
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that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine
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type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries
into structured search parameters.
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B: Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon
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Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When
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users submit natural language queries, convert the queries to embeddings by using the same FM. Perform
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k-nearest neighbors (k-NN) searches to find semantically similar results.
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 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: B
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
www.cert4prep.com/exam/aip-c01 Page 2 of 9
/
om
.c
pr AIP-C01
ep
t4
AWS Certified Generative AI Developer - Professional
er
.c
Version: 6.4
w
w
Questions & Answers DEMO PDF
w
(Preview content before you buy)
Check the full version using the link below
www.cert4prep.com/exam/aip-c01
Unlock Full Features
Stay Updated: 90 days of free exam updates
Zero Risk: 30-day money-back policy
Instant Access: Download right after purchase
Always Here: 24/7 customer support team
www.cert4prep.com/exam/aip-c01 Page 1 of 9
, Question 1. (Single Select)
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.
/
om
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
.c
solution must also scale cost-effectively during peak usage periods.
ep
Which solution will meet these requirements with the LEAST development effort?
pr
A: Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules
t4
that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine
er
type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries
into structured search parameters.
.c
B: Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon
w
Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When
w
users submit natural language queries, convert the queries to embeddings by using the same FM. Perform
w
k-nearest neighbors (k-NN) searches to find semantically similar results.
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 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: B
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
www.cert4prep.com/exam/aip-c01 Page 2 of 9
