Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes. The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:

Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3)

Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes)

Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)

To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:

Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.

Associate the role with the crawler.

Specify the S3 bucket path of the source data as the crawler’s data store. The crawler will scan the data and infer the schema and format2.

Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.

Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.

Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.

Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.

Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.

Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.

References:

1: AWS managed (predefined) policies for AWS Glue - AWS Glue

2: Data Catalog and crawlers in AWS Glue - AWS Glue

3: Scheduling an AWS Glue crawler - AWS Glue

[4]: Parameters set on Data Catalog tables by crawler - AWS Glue

[5]: AWS Glue pricing - Amazon Web Services (AWS)

Problem Analysis:

Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.

Processing time is increasing due to the additional testing facilities.

The goal is toreduce processing timewhile using the existing AWS Glue framework.

Key Considerations:

AWS Glue offers thedynamic frame file-groupingfeature, which consolidates small files into larger, more efficient datasets during processing.

Grouping smaller files reduces overhead and speeds up processing.

Solution Analysis:

Option A: Lambda for File Grouping

Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.

Option B: AWS Glue Dynamic Frame File-Grouping

This option directly addresses the issue by grouping small files during Glue job execution.

Minimizes data processing time with no extra overhead.

Option C: Redshift COPY Command

COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).

Option D: Amazon EMR

While EMR is powerful, replacing Glue with EMR increases operational complexity.

Final Recommendation:

UseAWS Glue dynamic frame file-groupingfor optimized data ingestion and processing.

References:

AWS Glue Dynamic Frames

Optimizing Glue Performance