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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Protect sensitive data - Encrypt data at rest and in transit - Implement access control and authentication
|
| Topic 2: Data Operations and Support | 22% | - Ensure reliability and scalability - Automate operational tasks - Monitor and troubleshoot data pipelines
|
| Topic 3: Data Store Management | 26% | - Design and implement data storage solutions
- Manage data lifecycle and storage tiers |
| Topic 4: Data Ingestion and Transformation | 34% | - Ingest data from various sources
|
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NEW QUESTION # 241
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option A is correct because AWS Glue is a serverless ETL service built to run PySpark workloads at scale with minimal infrastructure management. AWS documentation states that you can install additional Python modules and libraries for use with AWS Glue ETL jobs, including by using the --additional-python-modules parameter and Amazon S3 paths for wheel artifacts or other supported package delivery methods. That directly addresses the requirement for custom internal libraries. Since the data is already in Amazon S3 and the result must be loaded into Amazon Redshift, Glue is a natural low-overhead fit for this daily transformation pipeline.
Option B and C require managing compute infrastructure or cluster lifecycle, which increases operational overhead. Option D is not the best fit because SageMaker Processing is designed primarily for ML-oriented data preparation, not as the standard AWS service for large-scale ETL into Redshift. The question explicitly asks for least operational overhead with scalable PySpark and custom libraries, and AWS Glue provides exactly that managed capability.
NEW QUESTION # 242
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?
Answer: C
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.
1: AWS Glue Workflows
2: AWS Glue and Amazon EMR
3: AWS Step Functions
AWS Lambda
Amazon Managed Workflows for Apache Airflow
NEW QUESTION # 243
A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor ' s data into the vendor ' s Amazon S3 bucket once each week.
Which solution will meet this requirement?
Answer: D
Explanation:
The Redshift UNLOAD command is specifically designed to export query results to Amazon S3, and AWS Glue can orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:
"Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.
NEW QUESTION # 244
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?
Answer: B
Explanation:
Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance.
By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.
The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:
Choosing a data distribution style
Choosing a data sort key
Working with primary keys
NEW QUESTION # 245
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:
Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore) Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog) AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)
NEW QUESTION # 246
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