Free AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 – Amazon AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 Practice Test Question

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• ○ Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.
• ○ Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
• ○ Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
• ○ Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.

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• ○ Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
• ○ Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
• ○ Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
• ○ Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

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• ○ Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
• ○ Increase the AWS Glue instance size by scaling up the worker type.
• ○ Convert the AWS Glue schema to the DynamicFrame schema class.
• ○ Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
• ○ Modify the IAM role that grants access to AWS glue to grant access to all S3 features.

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• ○ 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.

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• ○ 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 PySpark program 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.

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• ○ Create an AWS Lambda function to schedule a cron job to run the stored procedure.
• ○ Schedule and run the stored procedure by using the Amazon Redshift Data API in an Amazon EC2 Spot Instance.
• ○ Use query editor v2 to run the stored procedure on a schedule.
• ○ Schedule an AWS Glue Python shell job to run the stored procedure.

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• ○ Ensure that the trust policy of the Lambda function execution role allows EventBridge to assume the execution role.
• ○ Ensure that both the IAM role that EventBridge uses and the Lambda function's resource-based policy have the necessary permissions.
• ○ Ensure that the subnet where the Lambda function is deployed is configured to be a private subnet.
• ○ Ensure that EventBridge schemas are valid and that the event mapping configuration is correct.

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• ○ YourEnvironmentName-WebServer
• ○ YourEnvironmentName-Scheduler
• ○ YourEnvironmentName-DAGProcessing
• ○ YourEnvironmentName-Task

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

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• ○ CREATE TABLE new_table AS SELECT * FROM old_tables;
• ○ INSERT INTO new_table SELECT * FROM old_table;
• ○ CREATE TABLE new_table (LIKE old_table);
• ○ CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA;

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• ○ Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
• ○ Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
• ○ Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
• ○ Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.

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• ○ Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
• ○ Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
• ○ Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
• ○ Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

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• ○ Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
• ○ Add the S3 bucket as a resource that the QuickSight service role can access.
• ○ Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
• ○ Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
• ○ Add the KMS key as a resource that the QuickSight service role can access.

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• ○ Use Amazon Redshift ML to generate inventory recommendations.
• ○ Use SQL to invoke a remote SageMaker endpoint for prediction.
• ○ Use Amazon Redshift ML to schedule regular data exports for offline model training.
• ○ Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
• ○ Use Amazon Redshift as a file storage system to archive old inventory management reports.

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• ○ Use AWS Glue Python jobs to read and transform the CSV files.
• ○ Use an AWS Glue custom crawler to read and transform the CSV files.
• ○ Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
• ○ Use AWS Glue DataBrew recipes to read and transform the CSV files.

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• ○ Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.
• ○ Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.
• ○ Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.
• ○ Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.

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• ○ Add an inbound rule that allows all TCP traffic on all TCP ports. Set the security group as the source.
• ○ Add an inbound rule that allows all TCP traffic on all UDP ports. Set the private IP address of the RDS instance as the source.
• ○ Add an inbound rule that allows all TCP traffic on all TCP ports. Set the DNS name of the RDS instance as the source.
• ○ Replace the source of the existing SSH rule with the private IP address of the RDS instance. Create an outbound rule with the same source, destination, and protocol as the inbound SSH rule.

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• ○ Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.
• ○ Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.
• ○ Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.
• ○ Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

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• ○ Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AWS Glue for each day’s data.
• ○ Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.
• ○ Reduce the number of partitions by changing the partitioning schema from daily to monthly granularity.
• ○ Increase the processing capacity of Athena queries by allocating more compute resources.

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• ○ Use AWS Glue DataBrew to create the partitions for the AWS Glue table.
• ○ Use an AWS Lambda function to create the partitions for the AWS Glue table.
• ○ Set partition projection properties for the AWS Glue table.
• ○ Configure an AWS Glue crawler to run on a set schedule.

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• ○ Use the AWS Glue Detect PII transform with specific patterns.
• ○ Use Amazon Made with managed data identifiers.
• ○ Use an AWS Lambda function with custom regular expressions.
• ○ Use Amazon Athena with a SQL query to match the custom formats.

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• ○ Increase the memory of the Lambda functions to 512 MB.
• ○ Increase the number of retries by using the Maximum Retry Attempts setting.
• ○ Configure the Lambda functions to run in the company's VPC.
• ○ Increase the timeout value for the Lambda functions from 15 seconds to 30 seconds.

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• ○ Increase the number of data processing units (DPUs) in AWS Glue ETL jobs.
• ○ Use Amazon EMR to manage the ETL pipeline instead of AWS Glue.
• ○ Use AWS Step Functions to orchestrate a parallel workflow state.
• ○ Enable auto scaling for the AWS Glue ETL jobs.

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• ○ Use Amazon Kinesis Data Streams with provisioned capacity and AWS Lambda functions to perform custom transformations and to integrate with Amazon S3.
• ○ Use Amazon Data Firehose with built-in data transformations. Deliver the data directly to Amazon S3.
• ○ Use Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to perform complex processing and to integrate with Amazon S3.
• ○ Use Amazon Data Firehose and AWS Lambda functions to perform custom transformations and to deliver the data to Amazon S3.

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• ○ Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
• ○ Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
• ○ Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.
• ○ Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.

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• ○ Use an AWS Glue job to compact the files into a standard size of 512 MB at the end of each day. Run an AWS Glue crawler to update the Data Catalog.
• ○ Configure the Data Catalog to automatically compact the files every minute.
• ○ Configure Iceberg table properties to enable automatic compaction based on thresholds for file size and the number of files.
• ○ Implement a partition strategy in Amazon S3. Run an AWS Glue crawler to update the Data Catalog every 5 minutes.

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• ○ Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.
• ○ Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.
• ○ Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift Java Database Connectivity (JDBC) connector to establish a database connection. Use the database connection to perform all read and write operations.
• ○ Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.

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• ○ Use Amazon Data Firehose to capture the data. Store the data in an Amazon S3 bucket. Use the COPY command to load data into Amazon Redshift.
• ○ Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to capture the data. Use Amazon EMR to process the data. Use federated queries to access data in Amazon Redshift.
• ○ Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.
• ○ Use Amazon DynamoDB Streams to capture the data. Use AWS Glue to process the data. Use a zero-ETL integration to load the data into Amazon Redshift.

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• ○ Use Amazon S3 buckets to store the data. Use Amazon Athena to query and analyze the data. Assign each data team a separate S3 bucket prefix to optimize queries.
• ○ Use streams in Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to query and analyze the data. Assign each data team a separate stream to manage and consume.
• ○ Use Amazon OpenSearch Service clusters with indexing to query the data. Assign each data team a separate cluster to configure for storage and queries.
• ○ Use Amazon Aurora clusters that run on Aurora I/O-Optimized instances. Assign each data team a separate Aurora cluster to configure for storage and queries.

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• ○ Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
• ○ ColumnValues "Email” matches "[%@%.%]" with a threshold set to > 0.1
• ○ ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
• ○ UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1

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• ○ Use Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.
• ○ Set up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
• ○ Set up IAM roles for each project group with permissions to access all datasets across all AWS accounts. Use AWS CloudTrail to record data access activity.
• ○ Create separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.

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• ○ Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
• ○ Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
• ○ Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
• ○ Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS. Use Amazon Kendra for full-text search capabilities.