Question 1

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster. A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort. Which combination of steps will create a solution that meets these requirements? (Select THREE.)

Options :

A : Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

B : Use an AWS Glue crawler to catalog the data.

C :

Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

D :

Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

E :

Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

F : Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

Answer: B,D,F

Question 2

A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices
The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key
Which action will resolve this issue?

Options :

A : Change the partition key from facility ID to a randomly generated key

B : Increase the number of shards

C : Archive the data on the producers' side

D : Change the partition key from facility ID to capture date

Answer: B

Question 3

A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

Options :

A : Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.

B : Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.

C : Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.

D : Edit the permissions for the new S3 bucket from within the S3 console.

Answer: B

Question 4

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

Options :

A : Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.

B : Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.

C : Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

D : Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

Answer: B

Question 5

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: ''Command Failed with Exit Code 1.''
Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90--95% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.
What should the data engineer do to solve the failure in the MOST cost-effective way?

Options :

A : Change the worker type from Standard to G.2X.

B : Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature.

C : Increase the fetch size setting by using AWS Glue dynamics frame.

D : Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

Answer: B

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