A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.A data engineer notices that one of the fields in the source data includes values that are in JSON format.How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

Use the SUPER data type to store the data in the Amazon Redshift table.

Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.

Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)

Select * from Employee where Region ID='North America' and Department ID=20;

Select * from Employee where Department ID=20 and Region ID='North America';

Select * from Employee where Region ID='North America';

Select * from Employee where Region ID='North America' and Department ID=20;

Select * from Employee where Department ID=20 and Region ID='North America';

Select ' from Employee where Role ID=50;

Select * from Employee where Region ID='North America' and Role ID=50;

A car sales company maintains data about cars that are listed for sale in an are a. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.Which solution will meet these requirements MOST cost-effectively?

Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

Use an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.

Use a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

Use AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.

Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.What is the likely reason the AWS Glue job is reprocessing the files?

The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.

The maximum concurrency for the AWS Glue job is set to 1.

The data engineer incorrectly specified an older version of AWS Glue for the Glue job.

The AWS Glue job does not have a required commit statement.

A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.Which solution will meet this requirement with the LEAST effort?

Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.

Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format.

Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.

Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.

Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

Use scripts to scan data elements and to assign data classifications based on the format of the data.

A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.Which solution will meet these requirements with the LEAST operational overhead?

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.

A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.Which solution will meet these requirements with the LEAST implementation effort?

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 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.

A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)

The producer experienced network-related timeouts.

There was a change in the number of shards, record processors, or both.

The producer experienced network-related timeouts.

The stream's value for the IteratorAgeMilliseconds metric was too high.

There was a change in the number of shards, record processors, or both.

The AggregationEnabled configuration property was set to true.

The max_records configuration property was set to a number that was too high.

A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster. The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.Which combination of steps will meet these requirements with the LEAST development effort? (Select TWO.)

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Use Amazon OpenSearch to correlate the logs and traces.

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.

Use Amazon OpenSearch to correlate the logs and traces.

Use AWS Glue to correlate the logs and traces.

A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.The marketing team should have access to obfuscated claim information but should have full access to customer contact information.The claims team should have access to customer information for each claim that the team processes.The analytics team should have access only to obfuscated PII data.Which solution will enforce these data access requirements with the LEAST administrative overhead?

Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.

Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.

Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.

Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.

Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.

A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.Which solution will meet this requirement with LEAST operational overhead?

Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.

Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.

Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.

Use Amazon Kinesis Data Firehose and Amazon Comprehend to detect and remove PII.

Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.

A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.Which solution will meet these requirements?

Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.

Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service.

Configure an AW5 Glue job to have a source of Amazon DynamoDB and a destination of Amazon OpenSearch Service to transfer data in near real time.

Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.

Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.

A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-AccountThe company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.Which combination of steps will meet this requirement? (Select TWO.)

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.

Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.

Add the 53 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 Bl-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.

A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.A data engineer needs a solution to automatically delete logs that are older than 1 year.Which solution will meet these requirements with the LEAST operational overhead?

Create an AWS Lambda function to delete the logs after 1 year.

Define an S3 Lifecycle configuration to delete the logs after 1 year.

Create an AWS Lambda function to delete the logs after 1 year.

Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.

Configure an AWS Step Functions state machine to delete the logs after 1 year.

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.Which solution will meet these requirements with the LEAST development effort?

Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.

Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.

Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.

Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.

Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.Which solution will meet these requirements?

Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.

Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.

Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.

Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.

A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.Which solution will meet these requirements?

Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.

Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company's AWS account. Allow each internal consumer to access the stream.

Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.

Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances. Configure auto scaling for the EC2 instances.

A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights. The company wants to reduce Athena costs but does not want to recreate the data pipeline.Which solution will meet these requirements with the LEAST management effort?

Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.

Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.Which solution will meet this requirement with the LEAST manual intervention?

Configure the S3 bucket and S3 objects to allow access to Amazon Macie. Use automated sensitive data discovery in Macie.

Configure AWS CloudTrail to monitor S3 PUT operations. Inspect the CloudTrail trails to identify operations that save PII.

Create an AWS Lambda function to identify PII in S3 objects. Schedule the function to run periodically.

Create a table in AWS Glue Data Catalog. Write custom SQL queries to identify PII in the table. Use Amazon Athena to run the queries.

A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake. Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.Which solution will meet these requirements with the LEAST operational overhead?

Schedule an AWS Glue crawler to periodically update the Data Catalog.

Use the AWS Glue API to manually update the Data Catalog.

Run an MSCK REPAIR TABLE command in Athena.

Schedule an AWS Glue crawler to periodically update the Data Catalog.

Run a REFRESH TABLE command in Athena.

A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.Which solution will meet these requirements with the LEAST operational overhead?

Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).

Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.

Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).

Use Amazon Aurora PostgreSQL to load all the data into Aurora.

A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.Which solution will meet these requirements with the LEAST effort?

Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.

Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.

Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.

Amazon DEA-C01 Practice Test 4 - Free Online Exam Questions 121-151

A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

Become a Premium Member for full access

Unlock Premium Member

Amazon DEA-C01 Practice Test 4

Question

Amazon DEA-C01 Practice Tests

Practice Tests