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Braindump Data-Engineer-Associate Free & Data-Engineer-Associate Test Dates

Braindump Data-Engineer-Associate Free & Data-Engineer-Associate Test Dates

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q16-Q21):

NEW QUESTION # 16
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?

  • A. 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.
  • B. Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.
  • C. Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
  • D. Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

Answer: B

Explanation:
Redshift data sharing is a feature that enables you to share live data across different Redshift clusters without the need to copy or move data. Data sharing provides secure and governed access to data, while preserving the performance and concurrency benefits of Redshift. By setting up the sales team BI cluster as a consumer of the ETL cluster, the company can share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution also minimizes the usage of the computing resources of the ETL cluster, as the data sharing does not consume any storage space or compute resources from the producer cluster. The other options are either not feasible or not efficient. Creating materialized views or database views would require the sales team to have direct access to the ETL cluster, which could interfere with the critical analysis tasks. Unloading a copy of the data from the ETL cluster to an Amazon S3 bucket every week would introduce additional latency and cost, as well as create data inconsistency issues. References:
* Sharing data across Amazon Redshift clusters
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift


NEW QUESTION # 17
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?

  • A. 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.
  • B. 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 production AWS account.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. Reference:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams


NEW QUESTION # 18
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?

  • A. 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.
  • B. 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 production AWS account.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams


NEW QUESTION # 19
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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: C

Explanation:
* Problem Analysis:
* Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.
* Processing time is increasing due to the additional testing facilities.
* The goal is toreduce processing timewhile using the existing AWS Glue framework.
* Key Considerations:
* AWS Glue offers thedynamic frame file-groupingfeature, which consolidates small files into larger, more efficient datasets during processing.
* Grouping smaller files reduces overhead and speeds up processing.
* Solution Analysis:
* Option A: Lambda for File Grouping
* Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.
* Option B: AWS Glue Dynamic Frame File-Grouping
* This option directly addresses the issue by grouping small files during Glue job execution.
* Minimizes data processing time with no extra overhead.
* Option C: Redshift COPY Command
* COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).
* Option D: Amazon EMR
* While EMR is powerful, replacing Glue with EMR increases operational complexity.
* Final Recommendation:
* UseAWS Glue dynamic frame file-groupingfor optimized data ingestion and processing.
:
AWS Glue Dynamic Frames
Optimizing Glue Performance


NEW QUESTION # 20
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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.
Option A: 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.
DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.
Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.
Reference:
AWS Database Migration Service Documentation
AWS Glue Documentation


NEW QUESTION # 21
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