Exam AWS Certified Machine Learning - Specialty topic 1 question 194 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 194
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A data scientist is working on a model to predict a company's required inventory stock levels. All historical data is stored in .csv files in the company's data lake on Amazon S3. The dataset consists of approximately 500 GB of data The data scientist wants to use SQL to explore the data before training the model. The company wants to minimize costs.

Which option meets these requirements with the LEAST operational overhead?

A. Create an Amazon EMR cluster. Create external tables in the Apache Hive metastore, referencing the data that is stored in the S3 bucket. Explore the data from the Hive console.
B. Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data.
C. Create an Amazon Redshift cluster. Use the COPY command to ingest the data from Amazon S3. Explore the data from the Amazon Redshift query editor GUI.
D. Create an Amazon Redshift cluster. Create external tables in an external schema, referencing the S3 bucket that contains the data. Explore the data from the Amazon Redshift query editor GUI.

Show Suggested Answer

Suggested Answer: B 🗳️

by dunhill at Nov. 28, 2022, 4:39 p.m.

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dunhill

Highly Voted 1 year, 7 months ago

I think the answer is B. The others are quite expensive and complicated.

upvoted 6 times

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loict

Most Recent 9 months, 3 weeks ago

Selected Answer: B

A. NO - B is easier B. YES - works natively against S3 C. NO - no need to import S3 data to Redshift when Presto/Athena allows you to query directly D. NO - Redshift overkill

upvoted 1 times

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Mickey321

10 months, 1 week ago

Selected Answer: B

AWS Glue

upvoted 1 times

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kaike_reis

10 months, 3 weeks ago

Selected Answer: B

We want to use SQL to explore the 500GB database saved in S3. We also want to minimize costs and have the least headache with the operation. Letters A - C - D mean managed services, hence: headache. Correct alternative is letter B.

upvoted 1 times

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ADVIT

12 months ago

Selected Answer: B

B as "LEAST operational overhead"

upvoted 1 times

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ZSun

1 year, 2 months ago

The advantage of D is that Redshift, as a data warehouse, can handle large dataset(>1TB) and complex frequent query. In this example, 500GB dataset and infrequent query(I consider this just one-time ad-hoc query, just verify the data before training.) Athena would be a much better option.

upvoted 2 times

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oso0348

1 year, 3 months ago

Selected Answer: B

The option that meets these requirements with the LEAST operational overhead is option B: Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data. AWS Glue is a fully managed ETL service that can automatically discover and catalog metadata about data stored in various data stores, including Amazon S3. By using AWS Glue to crawl the S3 bucket, the data scientist can easily create tables in the AWS Glue Data Catalog, without needing to create or manage any infrastructure. Amazon Athena is an interactive query service that allows querying data stored in Amazon S3 using SQL. By using Amazon Athena, the data scientist can easily explore the data using SQL, without needing to set up any infrastructure.

upvoted 3 times

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