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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions:

1. A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

A) Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.
B) Specify the Target-Volume-in-GiB parameter for the existing topic.
C) Expand the storage of the Apache ZooKeeper nodes.
D) Update the MSK broker instance to a larger instance type. Restart the MSK cluster.

2. A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?

A) Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.
B) Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.
C) Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
D) Group tables and views for each department into dedicated databases. Manage permissions at the database level.

3. An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A) Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
B) Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
C) Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
D) Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
E) Use an S3 bucket that is in the same account that uses Athena to query the data.

4. A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1 and contains 10 billion records. The sales table for the second company is named Table S2 and contains 900 million records. The query becomes slow after the modification.
A data engineer must improve the query performance.
Which solutions will meet these requirements? (Select TWO)

A) Use Amazon Redshift Advisor to review and select optimizations to implement.
B) Use the KEY distribution style for both sales tables. Select a high-cardinality column to use for the join.
C) Use the Amazon Redshift query optimizer to review and select optimizations to implement.
D) Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
E) Use the KEY distribution style for both sales tables. Select a low-cardinality column to use for the join.

5. A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns.
The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded.
A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

A) Configure the third-party application to create the files in JSON format.
B) Load the JSON data into the Amazon Redshift table in a SUPER type column.
C) Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
D) Partition the order data in the S3 bucket based on order date.
E) Configure the third-party application to create the files in a columnar format.

Solutions: