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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions
|
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Enforce compliance and data governance
|
| Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Automate operational tasks - Ensure reliability and scalability |
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NEW QUESTION # 222
A data engineer needs to deploy a complex pipeline. The stages of the pipeline must run scripts, but only fully managed and serverless services can be used.
Answer: C
Explanation:
AWS Glue is a fully managed and serverless ETL platform that supports scripts in PySpark or Python shell jobs.
Workflows in Glue orchestrate multiple job stages without infrastructure management.
"Use AWS Glue Workflows to build and orchestrate complex multi-stage ETL pipelines using serverless AWS Glue jobs."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This meets the "serverless only" and "runs scripts" requirements precisely.
NEW QUESTION # 223
A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?
Answer: B
Explanation:
TheChoicestate type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.
"The Step FunctionsChoicestate lets you branch the execution flow depending on values in the state's input.
This allows you to run different processing logic based on dynamic conditions like values in the input JSON."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This makesChoicethe correct answer for content-driven conditional workflows.
NEW QUESTION # 224
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of tiles into a tact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the tact table.
Which solution will meet these requirements?
Answer: D
Explanation:
To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.
* Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.
Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.
References:
* Amazon Redshift COPY Command Documentation
NEW QUESTION # 225
A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
Answer: C,E
Explanation:
Option A and B are the correct answers because they meet the requirements most cost-effectively. Using an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically is a simple and scalable way to orchestrate the queries. Creating an AWS Step Functions workflow and adding two states to check the query status and invoke the next query is a reliable and efficient way to handle the long-running queries.
Option C is incorrect because using an AWS Glue Python shell job to invoke the Athena queries programmatically is more expensive than using a Lambda function, as it requires provisioning and running a Glue job for each query.
Option D is incorrect because using an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully is not a cost- effective or reliable way to orchestrate the queries, as it wastes resources and time.
Option E is incorrect because using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch is an overkill solution that introduces unnecessary complexity and cost, as it requires setting up and managing an Airflow environment and an AWS Batch compute environment.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.2: AWS Lambda, Section 5.3: AWS Step Functions, Pages 125-135
* Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.1: AWS Lambda, Lesson 5.2: AWS Step Functions, Pages 1-15
* AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon Athena, Pages 1-4
* AWS Documentation Overview, AWS Step Functions Developer Guide, Getting Started, Tutorial:
Create a Hello World Workflow, Pages 1-8
NEW QUESTION # 226
A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?
Answer: D
Explanation:
Redshift data sharing is a feature that enables you to share live data across different Redshift clusters without the need to copy or move data. Data sharing provides secure and governed access to data, while preserving the performance and concurrency benefits of Redshift. By setting up the sales team BI cluster as a consumer of the ETL cluster, the company can share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution also minimizes the usage of the computing resources of the ETL cluster, as the data sharing does not consume any storage space or compute resources from the producer cluster. The other options are either not feasible or not efficient. Creating materialized views or database views would require the sales team to have direct access to the ETL cluster, which could interfere with the critical analysis tasks.
Unloading a copy of the data from the ETL cluster to anAmazon S3 bucket every week would introduce additional latency and cost, as well as create data inconsistency issues. References:
Sharing data across Amazon Redshift clusters
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift
NEW QUESTION # 227
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