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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q31-Q36):

NEW QUESTION # 31
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?

• A. Configure an AWS Step Functions state machine to delete the logs after 1 year.
• B. Define an S3 Lifecycle configuration to delete the logs after 1 year.
• C. Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.
• D. Create an AWS Lambda function to delete the logs after 1 year.

Answer: D

Explanation:
* Problem Analysis:
* The company usesAWS Gluefor ETL pipelines and requires automaticdata quality checks during pipeline execution.
* The solution must integrate with existing AWS Glue pipelines and evaluatedata quality rules based on predefined thresholds.
* Key Considerations:
* Ensure minimal implementation effort by leveraging built-in AWS Glue features.
* Use a standardized approach for defining and evaluating data quality rules.
* Avoid custom libraries or external frameworks unless absolutely necessary.
* Solution Analysis:
* Option A: SQL Transform
* Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
* Increases operational overhead and deviates from Glue's declarative approach.
* Option B: Evaluate Data Quality Transform with DQDL
* AWS Glue provides a built-inEvaluate Data Quality transform.
* Allows defining rules inData Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
* Fully integrated with Glue Studio, making it the least effort solution.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library for data quality checks but requires custom code and integration.
* Increases implementation effort compared to Glue's native capabilities.
* Option D: Custom Transform with Great Expectations
* Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
* Final Recommendation:
* UseEvaluate Data Quality transformin AWS Glue.
* Define rules inDQDLfor checking thresholds, null values, or other quality criteria.
* This approach minimizes development effort and ensures seamless integration with AWS Glue.
:
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations

NEW QUESTION # 32
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)

• A. Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.
• B. Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on 53 events that the SQS queue receives.
• C. Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.
• D. Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.
• E. Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.

Answer: A,C

Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
A . Create an S3 event-based AWS Glue crawler:
An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.
Reference:
C . Use an AWS Lambda function to directly update the Data Catalog:
Lambda can be triggered by S3 events delivered to the SQS queue and can directly update the Glue Data Catalog, ensuring that new data is reflected in near real-time without running a full crawler.
Alternatives Considered:
B (Time-based schedule): Scheduling a crawler to run periodically adds unnecessary latency and operational overhead.
D (Manual crawler initiation): Manually starting the crawler defeats the purpose of automation.
E (AWS Step Functions): Step Functions add complexity that is not needed when Lambda can handle the updates directly.
AWS Glue Event-Driven Crawlers
Using AWS Lambda to Update Glue Catalog

NEW QUESTION # 33
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.
• B. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.
• C. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket.
  Create a pipeline in Apache Spark.
• D. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket.
  Create a pipeline in Apache Spark.

Answer: D

Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:
AWS Glue
[AWS Glue Data Catalog]
[AWS Glue Developer Guide]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION # 34
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?

• A. Select * from Sales where city_name - '$(San&EI)";
• B. Select * from Sales where city_name -,

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