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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Topic 2: Data Ingestion and Transformation | 34% | - Apply programming concepts
|
| Topic 3: Data Security and Governance | 18% | - Apply authentication and authorization
|
| Topic 4: Data Store Management | 26% | - Choose a data store
|
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NEW QUESTION # 199
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?
Answer: A
Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue Studio is a graphical interface that allows you to easily author, run, and monitor AWS Glue ETL jobs. AWS Glue Data Quality is a feature that enables you to validate, cleanse, and enrich your data using predefined or custom rules. AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows.
Using the Detect PII transform in AWS Glue Studio, you can automatically identify and label the PII in your dataset, such as names, addresses, phone numbers, email addresses, etc. You can then create a rule in AWS Glue Data Quality to obfuscate the PII, such as masking, hashing, or replacing the values with dummy data. You can also use other rules to validate and cleanse your data, such as checking for null values, duplicates, outliers, etc. You can then use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. You can use AWS Glue DataBrew to visually explore and transform the data, AWS Glue crawlers to discover and catalog the data, and AWS Glue jobs to load the data into the S3 data lake.
This solution will meet the requirement with the least operational effort, as it leverages the serverless and managed capabilities of AWS Glue, AWS Glue Studio, AWS Glue Data Quality, and AWS Step Functions. You do not need to write any code to identify or obfuscate the PII, as you can use the built-in transforms and rules in AWS Glue Studio and AWS Glue Data Quality. You also do not need to provision or manage any servers or clusters, as AWS Glue and AWS Step Functions scale automatically based on the demand.
The other options are not as efficient as using the Detect PII transform in AWS Glue Studio, creating a rule in AWS Glue Data Quality, and using an AWS Step Functions state machine. Using an Amazon Kinesis Data Firehose delivery stream to process the dataset, creating an AWS Lambda transform function to identify the PII, using an AWS SDK to obfuscate the PII, and setting the S3 data lake as the target for the delivery stream will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. Using the Detect PII transform in AWS Glue Studio to identify the PII, obfuscating the PII, and using an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake will not be as effective as creating a rule in AWS Glue Data Quality to obfuscate the PII, as you will need to manually obfuscate the PII after identifying it, which can be error-prone and time-consuming. Ingesting the dataset into Amazon DynamoDB, creating an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data, and using the same Lambda function to ingest the data into the S3 data lake will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. You will also incur additional costs and complexity by using DynamoDB as an intermediate data store, which may not be necessary for your use case. Reference:
AWS Glue
AWS Glue Studio
AWS Glue Data Quality
[AWS Step Functions]
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue
NEW QUESTION # 200
A company needs to use an AWS Glue PySpark job to read specific data from an Amazon DynamoDB table.
The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data.
Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?
Answer: D
Explanation:
Amazon DynamoDB is optimized for Query operations, which retrieve items efficiently using the partition key and optionally the sort key. Queries consume significantly fewer RCUs than Scan operations because only matching partitions are read.
By performing a Query using the partition key in the key condition expression, the AWS Glue job reads only the required items directly from DynamoDB. This minimizes the number of read capacity units consumed and avoids unnecessary reads.
Using a Scan operation, even when followed by filtering in a DynamicFrame, consumes RCUs for the entire table or index and is highly inefficient. Using only the sort key in a query is invalid without the partition key and would not meet DynamoDB query requirements. The Glue DynamoDB connector filter option still performs a scan under the hood and does not minimize RCUs.
The queried data can be directly loaded into a DynamicFrame, satisfying the existing processing logic requirement without additional transformations.
Therefore, Option D provides the most efficient and cost-effective solution.
NEW QUESTION # 201
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: D
Explanation:
The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.
Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.
DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.
Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.
Reference:
AWS Database Migration Service Documentation
AWS Glue Documentation
NEW QUESTION # 202
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?
Answer: B
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. Reference:
AWS Glue
AWS Glue Data Catalog
AWS Glue Workflows
[AWS Glue Job Bookmarking]
[AWS Glue Crawlers]
[AWS Glue Schema Registry]
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 203
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?
Answer: C
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 # 204
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