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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q23-Q28):
NEW QUESTION # 23
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
Answer:
Explanation:
Inspect the job run monitoring section of the AWS Glue console.
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.
NEW QUESTION # 24
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) Job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?
- A. Convert the sort-merge join in the job to be a broadcast join.
- B. Deploy an Amazon Elastic Block Store (Amazon EBS) volume for the job to use.
- C. Use the AWS Glue Spark shuffle manager.
- D. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.
Answer: D
NEW QUESTION # 25
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?
- A. AWS Glue workflows
- B. AWS Lambda functions
- C. AWS Step Functions tasks
- D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows
Answer: A
Explanation:
AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows. You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.
The other options are not optimal for the following reasons:
B . AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.
C . AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.
D . Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs). You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows. However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.
Reference:
1: AWS Glue Workflows
2: AWS Glue and Amazon EMR
3: AWS Step Functions
: AWS Lambda
: Amazon Managed Workflows for Apache Airflow
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 Amazon S3 to store the JSON data. Use Amazon Athena to query the data.
- B. Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.
- C. Use the SUPER data type to store the data in the Amazon Redshift table.
- D. Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
Answer: C
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 has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. After a merger, a query joining two large sales tables becomes slow. Table S1 has 10 billion records, Table S2 has 900 million records.
The query performance must improve.
- A. Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.
- B. Use the Amazon Redshift query optimizer to review and select optimizations to implement.
- C. Use Amazon Redshift Advisor to review and select optimizations to implement.
- D. Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
- E. Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
Answer: C,D
Explanation:
To optimize joins between large tables, Redshift recommends using KEY distribution on a common, high- cardinality join key to ensure co-location of data blocks. The Amazon Redshift Advisor identifies performance bottlenecks and provides recommendations for distribution and sort keys.
"Use KEY distribution when joining large tables on a common high-cardinality column. Use Redshift Advisor to identify query optimization opportunities."
NEW QUESTION # 28
......
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