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Amazon MLA-C01 Exam - Topic 1 Question 6 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 6
Topic #: 1
[All MLA-C01 Questions]

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

Which solution will meet these requirements?

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Suggested Answer: C

by Yuki at Jun 13, 2025, 05:16 PM

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Rozella
4 months ago
C is definitely the way to go for efficiency!
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Tamala
4 months ago
I agree, Kendra is designed for that purpose!
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Trina
5 months ago
B could work too, but it feels more complex than necessary.
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Kiera
5 months ago
Wait, can Textract really handle semantic searches? Sounds off.
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Tamekia
5 months ago
Option C seems like the best fit for semantic search with Kendra.
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Ilona
5 months ago
I vaguely recall that Kendra has built-in capabilities for semantic search, which could simplify the implementation.
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Shawnna
5 months ago
I feel like using Amazon Textract might not be the best option here since it’s more focused on extracting text rather than semantic search.
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Dorsey
6 months ago
I'm not entirely sure, but I think AWS Batch might be overkill for just generating embeddings. We practiced a similar question with SageMaker before, though.
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Cristal
6 months ago
I remember we discussed using Amazon Kendra for semantic search in class. It seems like a good fit for this scenario.
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Louisa
6 months ago
I'm a bit confused by the question. It's talking about a RAG application, but none of the options seem to directly address that. I'm not sure if I'm missing something or if the question is just not very clear. I might need to ask the instructor for some clarification before I can confidently select an answer.
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Tijuana
6 months ago
Okay, let me think this through. We need to migrate the RAG application to AWS and implement a semantic search solution for the text files in S3. Option C with Kendra seems like the most straightforward approach, as it's designed specifically for this type of use case. I think I'll go with that unless I can find a compelling reason why one of the other options might be better.
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Malika
6 months ago
Hmm, I'm a bit unsure about this one. The question is asking for a solution that provides semantic search, but it's not clear to me how the other options like AWS Batch, SageMaker, and Textract would handle that. I might need to do some more research on the capabilities of each service to decide which one is the best fit.
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Melita
7 months ago
This seems like a pretty straightforward question. I think I'll go with option C - using the Amazon Kendra S3 connector to ingest the documents and then querying Kendra for the semantic searches. That seems like the most direct solution to the requirements.
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Rozella
9 months ago
I'll go with C, but I'm secretly hoping the answer is D just so I can use the word 'asynchronous' in an exam question.
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Alyce
9 months ago
Haha, I bet the person who wrote this question is a big fan of Kendra. C is definitely the easiest answer, but where's the fun in that?
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Pa
9 months ago
Hmm, I'm not sure about C. I think B might be a more flexible solution, allowing for more customization with the SageMaker notebook and Feature Store.
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Moon
7 months ago
Yeah, using SageMaker for generating embeddings seems like a flexible solution.
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Rolland
8 months ago
I agree, B sounds like a good option for more customization.
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Emelda
8 months ago
Yeah, using SageMaker for generating embeddings seems like a flexible solution.
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Rhea
9 months ago
I agree, B sounds like a good option for more customization.
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Corinne
9 months ago
I agree, C is the way to go. Kendra is specifically designed for this kind of use case, and it should handle the semantic search requirements nicely.
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Rene
8 months ago
I think C is the best option too. Kendra seems like the most efficient solution for this scenario.
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Johnetta
8 months ago
C) Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
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Helga
10 months ago
Option C seems like the most straightforward solution here. Kendra's S3 connector should make it easy to ingest the documents and provide semantic search capabilities.
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France
9 months ago
Yeah, Kendra's S3 connector will definitely simplify the process of ingesting the documents and performing semantic searches.
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Zoila
9 months ago
I agree, using Amazon Kendra for semantic search sounds like the best approach for this scenario.
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Rory
9 months ago
Option C seems like the most straightforward solution here. Kendra's S3 connector should make it easy to ingest the documents and provide semantic search capabilities.
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Timothy
10 months ago
I'm leaning towards option B. Using SageMaker for generating embeddings seems like a solid approach.
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Lizbeth
10 months ago
I disagree, I believe option C is the way to go. Amazon Kendra is specifically designed for semantic searches.
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Frank
10 months ago
I think option A is the best choice because it uses AWS Batch and Glue for processing and storing embeddings.
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