Data Operations and Support

Automating data processing, analyzing data, maintaining and monitoring data pipelines, and ensuring data quality using AWS services.

5 minutes 5 Questions

Data Operations and Support is a critical domain in the AWS Certified Data Engineer - Associate exam, encompassing the practices, tools, and strategies needed to maintain, monitor, and optimize data pipelines and data infrastructure on AWS. **Key Areas:** 1. **Data Pipeline Maintenance:** This in…

Concepts covered
Orchestrating Data Pipelines with MWAA and Step FunctionsQuerying Data with Amazon AthenaData Visualization with Amazon QuickSightProvisioned vs. Serverless Service TradeoffsLogging and Monitoring with CloudWatchPipeline Troubleshooting and Performance TuningData Quality Rules and Validation ChecksData Sampling and Skew HandlingData Processing with EMR, Redshift, and GlueData Preparation with DataBrew and SageMakerLambda-Based Data Processing AutomationSQL Querying and Views in Redshift and AthenaData Aggregation, Grouping, and PivotingAuditing API Calls with CloudTrail
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AWS DEA-C01 - Data Operations and Support Example Questions

Test your knowledge of Data Operations and Support

Question 1

A team wants to know who created a specific AWS Glue database last week. They have CloudTrail enabled in their account. Where should they look to find this information?

Correct Answer: Check the CloudTrail Event history in the AWS Management Console for the CreateDatabase API call

AWS CloudTrail records API calls made in your AWS account, and the Event history in the AWS Management Console allows you to search and view the last 90 days of management events, including the Glue CreateDatabase API call, which will show who made the request and when.

AWS Glue does not have a built-in audit log section in its console. CloudWatch Logs metrics dashboard does not store API call records directly; CloudTrail is the service that logs API activity. Glue job run history tracks ETL job executions, not Data Catalog operations like database creation.

Key concept tested: Using CloudTrail Event history to audit AWS API activity and identify who performed specific actions.

Question 2

Which query engine does Amazon Athena use to execute SQL queries against data stored in Amazon S3?

Correct Answer: Athena uses an engine based on Trino (formerly PrestoSQL) to process SQL queries

Amazon Athena is built on a query engine based on Trino (formerly known as PrestoSQL), which is an open-source distributed SQL query engine designed for fast, interactive analytics over large datasets in Amazon S3.

While Athena does support Apache Spark for notebook-based analytics, its core SQL query engine is Trino-based, not Spark SQL. Apache Hive is used by Athena for its metastore (AWS Glue Data Catalog) and DDL syntax, but the actual query execution engine is not Hive MapReduce. Redshift Spectrum is a separate AWS service feature within Amazon Redshift and is not related to Athena's engine.

Key concept tested: understanding Athena's underlying query engine architecture.

Question 3

A team wants to query data stored in Amazon S3 using Amazon Athena. A new team member asks where the query results are saved after running a query. Where does Amazon Athena store query results by default?

Correct Answer: In an Amazon S3 bucket specified in the Athena query result settings

Amazon Athena stores all query results in an Amazon S3 bucket that is specified in the Athena query result settings, either at the workgroup level or in the individual client settings. Users must configure this S3 output location before running queries, and Athena writes results as CSV files to that location.

DynamoDB is not used by Athena for storing query results. Athena is serverless and does not provision any RDS instances. AWS Glue Data Catalog serves as the metadata store for Athena but does not manage query result output.

The key concept tested is understanding Athena's query result storage configuration and its reliance on S3 for output.

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