Amazon DEA-C01 Online Dumps Boost Your Career

Category:

Comments:

0 Comments

Post Date:

February 9, 2025

The Amazon DEA-C01 AWS Certified Data Engineer - Associate (DEA-C01) certification exam is challenging, and it's natural to feel anxious and nervous before taking the exam. Practicing with Amazon DEA-C01 dumps questions can help alleviate this anxiety by boosting your confidence. As you practice and become more familiar with the exam format and content, you'll feel more confident in your abilities, which can help you perform better on the actual Amazon DEA-C01 exam. Practicing with Amazon DEA-C01 questions can increase your chances of success in the certification exam. Practice free Amazon Amazon DEA-C01 exam dumps below.

Page 1 of 5

1. A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.

A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.

Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

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 1AM role that grants access to AWS glue to grant access to all S3 features.

2. A retail company uses Amazon Aurora PostgreSQL to process and store live transactional dat a. The company uses an Amazon Redshift cluster for a data warehouse.

An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.

A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.

Which combination of steps will meet these requirements? (Select TWO.)

Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.

Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift duster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.

Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

3. A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.

Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.

Which solution will meet this requirement with the LEAST latency?

Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

4. A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.

To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.

Which solution will meet the requirements with the LEAST operational overhead?

Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

5. A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.

Which solution will meet these requirements with the LEAST operational overhead?

Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.

Create an AWS Lambda Python function with provisioned concurrency.

Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.

6. 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.

7. 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 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.

8. A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.

Which solution will meet these requirements?

Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.

Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.

Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.

9. A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.

The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.

The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.

Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

A. Configure the third-party application to create the files in a columnar format.

B. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

C. Partition the order data in the S3 bucket based on order date.

D. Configure the third-party application to create the files in JSON format.

E. Load the JSON data into the Amazon Redshift table in a SUPER type column.

10. 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

Page 2 of 5

11. A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.

A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.

Which solution will meet these requirements with the LEAST operational overhead?

Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day

Use the query result reuse feature of Amazon Athena for the SQL queries.

Add an Amazon ElastiCache cluster between the Bl application and Athena.

Change the format of the files that are in the dataset to Apache Parquet.

12. 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.

13. 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 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.

14. 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?

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.

15. A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.

Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.

Which combination of solutions will meet these requirements? (Select TWO.)

A. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

B. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

C. Configure an Amazon Made discovery job for the S3 bucket.

D. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.

E. Write custom scripts in an application to mask the PII data and to control access.

16. A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.

The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.

Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

B. Use Amazon Redshift Serverless to automatically process the analytics workload.

C. Use the AWS CLI to automatically process the analytics workload.

D. Use AWS CloudFormation templates to automatically process the analytics workload.

17. A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day. Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.

Which solution will meet these requirements in the MOST operationally efficient way?

Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day.

Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.

18. A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.

Which AWS Glue feature should the data engineer use to meet this requirement?

Workflows

Triggers

Job bookmarks

Classifiers

19. A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.

The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.

Which solution will meet these requirements?

Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

20. 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.

Page 3 of 5

21. A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.

Which solution will meet these requirements with the LEAST operational overhead?

Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.

Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

22. 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.

23. A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.

The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.

Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

A. Turn on the public access setting for the DB instance.

B. Update the security group of the DB instance to allow only Lambda function invocations on the

database port.

C. Configure the Lambda function to run in the same subnet that the DB instance uses.

D. Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.

E. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

24. A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.

The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.

Which solution will meet these requirements MOST cost-effectively?

Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

Configure a Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.

Configure an external Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use Amazon Aurora MySQL to store the company's data catalog.

Configure a new Hive metastore in Amazon EM

Migrate the existing on-premises Hive metastore into Amazon EM

Use the new metastore as the company's data catalog.

25. A car sales company maintains data about cars that are listed for sale in an area. 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 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.

26. A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.

The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.

Which solution will meet these requirements with the LOWEST latency?

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.

D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

27. 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 data from 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.

28. 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.

29. A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.

A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.

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

Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.

Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.

Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.

Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

30. A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.

Which solution will meet these requirements with the LEAST operational overhead?

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.

Page 4 of 5

31. 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.

32. 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 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.

33. A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.

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

Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.

Create a trail of management events in AWS CloudTrai

Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.

Create a trail of data events in AWS CloudTrai

Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

34. A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.

The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.

A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.

Which solution will meet these requirements with the LEAST operational effort?

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 EM

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.

35. A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.

Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

Configure AWS Glue triggers to run the ETL jobs even/ hour.

Use AWS Glue DataBrewto clean and prepare the data for analytics.

Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.

Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.

Use the Redshift Data API to load transformed data into Amazon Redshift.

36. Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source. The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.

Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

git diff branchB master git commit -m

git pull master

git rebase master

git fetch -b master

37. A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.

A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.

Which solution will meet this requirement?

Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.

Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.

Design the data source so events are not ingested into Kinesis Data Streams multiple times.

Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EM

38. A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.

Which AWS service or feature will meet these requirements MOST cost-effectively?

AWS Step Functions

AWS Glue workflows

AWS Glue Studio

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

39. A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.

Which solution will meet these requirements with the LEAST ongoing maintenance?

Use the DynamoDB TTL feature to automatically expire data based on timestamps.

Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.

Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.

Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.

40. A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.

A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.

Which solution will meet this requirement?

Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.

Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.

Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.

Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

Page 5 of 5

41. A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.

Which solution will run the Glue jobs in the MOST cost-effective way?

Choose the FLEX execution class in the Glue job properties.

Use the Spot Instance type in Glue job properties.

Choose the STANDARD execution class in the Glue job properties.

Choose the latest version in the GlueVersion field in the Glue job properties.

42. A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.

Which solution will meet this requirement?

Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.

Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

43. A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.

Which SQL statement should the data engineer use to meet this requirement?

Option A

Option B

Option C

Option D

44. A company uses an on-premises Microsoft SQL Server database to store financial transaction dat a. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.

The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.

Which AWS service should the company use to meet these requirements?

AWS Lambda

AWS Database Migration Service (AWS DMS)

AWS Direct Connect

AWS DataSync

45. 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.

46. A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.

The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.

Which solution will meet these requirements in the MOST operationally efficient way?

Kinesis Agent

Kinesis Producer Library (KPL)

Amazon Data Firehose

Kinesis SDK

TAGS:

Amazon DEA-C01, Amazon DEA-C01 exam dumps

Notify of

Label

Name*

Email*

Website

Label

Name*

Email*

Website

0 Comments

Oldest

Newest Most Voted

Inline Feedbacks

View all comments

Get Amazon DEA-C01 Dumps Full Version

Q&As: 152
Versions: PDF and Software Download Now

About Dumpsinfo

Dumpsinfo is a good platform providing the latest exam information and dumps questions for all IT certification exams. You can study all the latest exam dumps questions online.

[email protected]

Mon - Sat 9:00am - 6:00pm

Amazon DEA-C01 Online Dumps Boost Your Career

Related

Posts

Improve Your Knowledge with MLA-C01 Exam Dumps

Amazon AIF-C01 Exam Questions Simulate Actual AIF-C01 Exam

Prepare SOA-C02 Exam with Using SOA-C02 Dump Questions

Online MLS-C01 Exam Dumps Help You Build Confidence

About Us

EMAIL

Services

Opening Hours