Data-Engineer-Associate Practice Questions

AWS Certified Data Engineer - Associate (DEA-C01)

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Question # 1

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?

Options:

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.

Discussion 0

Question # 2

A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.

A data engineer wants to use S3 Object Lock to secure the data.

Which solution will meet these requirements?

Options:

Enable governance mode on the S3 bucket. Use a default retention period of 7 years.

Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.

Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.

Set the retention period for individual objects in the S3 bucket to 7 years.

Discussion 0

Question # 3

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?

Options:

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.

Discussion 0

Question # 4

A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, " No space left on device. "

The data engineer needs to identify the source of the error and provide a solution.

Which combinations of steps will meet this requirement MOST cost-effectively? (Select TWO.)

Options:

Scale out the workers vertically to address data skewness.

Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.

Scale out the number of workers horizontally to address data skewness.

Enable the --write-shuffle-files-to-s3 job parameter. Use the salting technique.

Use error logs in Amazon CloudWatch to monitor data skew.

Discussion 0

Question # 5

A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The data stream runs in Account

The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account

What additional step must the company take to meet this requirement?

Options:

Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account

Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.

Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account

Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.

Discussion 0

Question # 6

A data engineer is launching an Amazon EMR cluster. 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?

Options:

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.

Discussion 0

Question # 7

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°

The data quality checks must also identify records that are missing timestamp values.

The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.

Which solution will meet these requirements?

Options:

Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

Use AWS Glue ' s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.

Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.

Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.

Discussion 0

Question # 8

A data engineer is processing a large amount of log data from web servers. The data is stored in an Amazon S3 bucket. The data engineer uses AWS services to process the data every day. The data engineer needs to extract specific fields from the raw log data and load the data into a data warehouse for analysis.

Options:

Use Amazon EMR to run Apache Hive queries on the raw log files in the S3 bucket to extract the specified fields. Store the output as ORC files in the original S3 bucket.

Use AWS Step Functions to orchestrate a series of AWS Batch jobs to parse the raw log files. Load the specified fields into an Amazon RDS for PostgreSQL database.

Use an AWS Glue crawler to parse the raw log data in the S3 bucket and to generate a schema. Use AWS Glue ETL jobs to extract and transform the data and to load it into Amazon Redshift.

Use AWS Glue DataBrew to run AWS Glue ETL jobs on a schedule to extract the specified fields from the raw log files in the S3 bucket. Load the data into partitioned tables in Amazon Redshift.

Discussion 0

Question # 9

A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Question # 9

Which solution will meet this requirement with the LEAST coding effort?

Options:

Use AWS Glue DataBrew to read the files. Use the NEST TO ARRAY transformation to create the new column.

Use AWS Glue DataBrew to read the files. Use the NEST TO MAP transformation to create the new column.

Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.

Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

Discussion 0

Question # 10

A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.

A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.

Which solution will meet this requirement?

Options:

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

Discussion 0

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