Free AWS Certified Data Engineer – Associate Exam Amazon-DEA-C01 Exam Practice Test

A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.Which solution will MOST speed up the Athena query performance?

• Change the data format from .csvto JSON format. Apply Snappy compression.
• Compress the .csv files by using Snappy compression.
• Change the data format from .csvto Apache Parquet. Apply Snappy compression.
• Compress the .csv files by using gzjg compression.

A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation. Which solution will meet these requirements with the LEAST operational overhead?

• Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
• Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
• Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.
• Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.Which solution will meet these requirements with the LEAST operational overhead?

• Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
• Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
• Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed datafrom the S3 bucket.
• Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.Which solution will meet these requirements?

• Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
• Change the distribution key to the table column that has the largest dimension.
• Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.
• Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.Which combination of troubleshooting steps should the data engineer take? (Select TWO.)

• Confirm that Athena is pointing to the correct Amazon S3 location.
• Increase the query timeout duration.
• Use the MSCK REPAIR TABLE command.
• Restart Athena.
• Delete and recreate the problematic Athena table.

A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.Which solution will meet these requirements with the LEAST operational overhead?

• Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
• Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi- node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.Which solution will meet these requirements?

• Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
• Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.
• Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
• Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.Which actions should the data engineer take to meet this requirement? (Choose two.)

• Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
• Modify the database schema to include additional tables and indexes.
• Reboot the RDS DB instance once each week.
• Upgrade to a larger instance size.
• Implement caching to reduce the database query load.

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data. Which solution will meet these requirements with the LEAST operational overhead?

• Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
• Use the streaming ingestion feature of Amazon Redshift.
• Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
• Use the Amazon Aurora zero-ETL integration with Amazon Redshift.

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.Which solution will meet these requirements MOST cost-effectively?

• Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
• Write a PySpark ETL script. Host the script on an Amazon EMR cluster.
• Write an AWS Glue PySpark job. Use Apache Spark to transform the data.
• Write an AWS Glue Python shell job. Use pandas to transform the data.