Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) Pass4sure Zertifizierung & AWS Certified Data Engineer - Associate (DEA-C01) zuverlässige Prüfung Übung

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Operations and Support	22%	- Monitor and maintain data pipelines - Troubleshoot data workflow issues
Topic 2: Data Store Management	26%	- Select appropriate data storage solutions - Optimize storage performance and cost
Topic 3: Data Security and Governance	18%	- Implement data security controls - Apply governance and compliance best practices
Topic 4: Data Ingestion and Transformation	34%	- Build and manage data pipelines - Ingest and transform data using AWS services

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Die neuesten Data-Engineer-Associate echte Prüfungsfragen, Amazon Data-Engineer-Associate originale fragen

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q50-Q55):

50. Frage
A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.
Which SQL statement should the data engineer use to meet this requirement?

Antwort: C

Begründung:
* Problem Analysis:
* The goal is to create anew empty tablein Athena with the same schema as an existing table (old_table).
* The solution must avoid copying any data.
* Key Considerations:
* CREATE TABLE AS (CTAS)is commonly used in Athena for creating new tables based on an existing table.
* Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.
* Solution Analysis:
* Option A: Copies both schema and data. Does not meet the requirement for an empty table.
* Option B: Inserts data into an existing table, which does not create a new table.
* Option C: Creates an empty table but does not copy the schema.
* Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.
* Final Recommendation:
* UseD. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATAto create an empty table with the same schema.
:
Athena CTAS Queries
CREATE TABLE Statement in Athena

51. Frage
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?

A. Workflows
B. Classifiers
C. Job bookmarks
D. Triggers

Antwort: C

Begründung:
Problem Analysis:
The pipeline processes compressed files in S3 and must support incremental data processing.
AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.
Key Considerations:
Incremental data processing requires tracking which files or partitions have already been processed.
The solution must be automated and efficient for large-scale ETL jobs.
Solution Analysis:
Option A: Workflows
Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.
Option B: Triggers
Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.
Option C: Job Bookmarks
Job bookmarks track the state of the data that has been processed, enabling incremental processing.
Automatically skip files or partitions that were previously processed in Glue jobs.
Option D: Classifiers
Classifiers determine the schema of incoming data but do not handle incremental processing.
Final Recommendation:
Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.
Reference:
AWS Glue Job Bookmarks Documentation
AWS Glue ETL Features

52. Frage
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.
B. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
C. Build an Amazon EventBridge-based pipeline that invokes AWS Lambda functions to perform each stage.
D. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data. Use AWS Glue jobs to transform the data and store the data in the S3 bucket.

Antwort: D

Begründung:
AWS Step Functions is a fully managed, serverless orchestration service designed to coordinate multiple AWS services into reliable workflows. Step Functions natively provide automatic retries, error handling, state-level visibility, and execution history, which directly satisfy the requirements for retry logic and monitoring individual pipeline stages.
In this solution, AWS Lambda functions are used for batch data ingestion, AWS Glue jobs handle transformation, and Amazon S3 is used for storage. Step Functions orchestrate these stages while maintaining clear separation of responsibilities. Each step can be configured with retry and catch policies, allowing the workflow to automatically recover from transient failures without manual intervention.
Chaining AWS Glue jobs alone does not provide detailed stage-level visibility or flexible retry control, and setting MaxRetries to 0 contradicts the retry requirement. EventBridge-based pipelines lack native workflow state tracking. Amazon MWAA introduces unnecessary operational overhead, including environment management and Airflow maintenance.
Therefore, using AWS Step Functions to orchestrate Lambda and Glue provides the most scalable, observable, and low-maintenance solution.

53. Frage
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
B. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.
C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
D. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

Antwort: D

Begründung:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions. S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration. Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics. Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena

54. Frage
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. 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.
B. 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.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
D. 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.

Antwort: B

Begründung:
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.

55. Frage
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