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

A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.Which Amazon Redshift command will meet these requirements?

• VACUUM FULL Orders
• VACUUM DELETE ONLY Orders
• VACUUM REINDEX Orders
• VACUUM SORT ONLY Orders

A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.The data engineer requires a less manual way to update the Lambda functions. Which solution will meet this requirement?

• Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
• Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambdafunctions.
• Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
• Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.

A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.Which solution will meet this requirement with the LEAST operational overhead?

• Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
• Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
• Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.
• Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

A data engineer is launching an Amazon EMR duster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.Which solution will meet these requirements?

• Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
• Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
• Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
• Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach the security configuration to the cluster.

A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.The data engineer needs a solution that will prevent unintentional file deletion in the future. Which solution will meet this requirement with the LEAST operational overhead?

• Manually back up the S3 bucket on a regular basis.
• Enable S3 Versioning for the S3 bucket.
• Configure replication for the S3 bucket.
• Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.Which solution will meet these requirements with the LEAST operational overhead?Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.Use S3 Intelligent-Tiering. Use the default access tier.

• S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost- optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency.

A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.Which solution will meet these requirements?

• Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
• Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.
• Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2.
• Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.

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.

A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.Which solution will give the company the ability to use Spark to access Athena?

• Athena query settings
• Athena workgroup
• Athena data source
• Athena query editor

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.Which solution will meet these requirements MOST cost-effectively?

• Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
• Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
• Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
• Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.