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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q25-Q30):

NEW QUESTION # 25
A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?

A. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
B. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In- memory Calculation Engine (SPICE). Create the required analyses and dashboards in QuickSight.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.
D. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.

Answer: D

Explanation:
The optimal solution for near-real-time querying and visualization of logs is to integrateAmazon CloudWatch LogswithAmazon OpenSearch Serviceusingsubscription filters, which stream the logs directly into OpenSearch for querying and dashboarding:
"Use OpenSearch Service with CloudWatch Logs and create a subscription filter to stream log data in near real time into OpenSearch. Then use OpenSearch dashboards for visualization."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This approach offers low latency and avoids batch exports, unlike the scheduled Athena + S3 pattern.

NEW QUESTION # 26
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?

A. Use the SUPER data type to store the data in the Amazon Redshift table.
B. Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
C. Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.
D. Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

Answer: A

Explanation:
In Amazon Redshift, theSUPERdata type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. TheSUPERdata type also works seamlessly withRedshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company's need to use combined datasets to identify potential new customers.
Using the SUPER data type also allows forautomatic parsing and query processingof nested data structures through Amazon Redshift'sPARTITION BYandJSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.
:
Amazon Redshift Documentation - SUPER Data Type
AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS AWS Certified Data Engineer - Associate Study Guide By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.

NEW QUESTION # 27
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
B. Use an AWS Glue workflow to refresh the materialized views.
C. Use Apache Airflow to refresh the materialized views.
D. Use the query editor v2 in Amazon Redshift to refresh the materialized views.

Answer: A

Explanation:
The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead.
Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:
* Query editor V2
* Working with materialized views
* Scheduling queries
* [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

NEW QUESTION # 28
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 Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
B. Write custom scripts in an application to mask the PII data and to control access.
C. Configure an Amazon Made discovery job for the S3 bucket.
D. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.
E. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

Answer: A,D

Explanation:
To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.
A . AWS Glue DataBrew for Masking:
AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.
Reference:
D . AWS Identity and Access Management (IAM):
Using IAM policies allows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.
Alternatives Considered:
B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.
C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.
E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.
AWS Glue DataBrew for Data Masking
IAM Policies for PII Access Control

NEW QUESTION # 29
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?

A. Schedule an AWS Glue crawler to periodically update the Data Catalog.
B. Run a REFRESH TABLE command in Athena.
C. Run an MSCK REPAIR TABLE command in Athena.
D. Use the AWS Glue API to manually update the Data Catalog.

Answer: A

Explanation:
Explanation: Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.

NEW QUESTION # 30
......

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Amazon Test Data-Engineer-Associate Topics Pdf Exam Pass at Your First Attempt | Associate Data-Engineer-Associate Level Exam

Associate Data-Engineer-Associate Level Exam & Data-Engineer-Associate Valid Braindumps Files

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q25-Q30):

100% Pass Data-Engineer-Associate - Authoritative Test AWS Certified Data Engineer - Associate (DEA-C01) Topics Pdf