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

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.

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)

The Redshift UNLOAD command is specifically designed to export query results to Amazon S3, and AWS Glue can orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:

“Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:

AWS Glue triggers

AWS Glue connections

AWS Glue DataBrew

[AWS Lambda functions]

[Redshift Data API]