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

Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.

The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. References:

Using Apache Spark in Amazon Athena

Athena JDBC Driver

Spark SQL

Athena query settings

[Athena workgroups]

[Athena query editor]

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)

Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.

Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.

“For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching.”

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