AWS AIP-C01 Exam
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, 1.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. 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.
B. 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.
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
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
AWS Certified Generative AI Developer -
Professional
https://www.passquestion.com/aip-c01.html
35% OFF on All, Including AIP-C01 Questions and Answers
Pass AIP-C01 Exam with PassQuestion AIP-C01 questions and
answers in the first attempt.
https://www.passquestion.com/
, 1.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. 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.
B. 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.
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
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
