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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Manage data privacy and compliance
|
| Topic 2: Data Store Management | 26% | - Understand data cataloging
|
| Topic 3: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Topic 4: Data Ingestion and Transformation | 34% | - Apply programming concepts
|
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NEW QUESTION # 99
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: A,B
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.
Reference:
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 # 100
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: B
Explanation:
Problem Analysis:
The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
Key Considerations:
The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.
Efficiently handles high-throughput scenarios with minimal operational overhead.
Solution Analysis:
Option A: Kinesis Agent
Designed for file-based ingestion; not optimized for geolocation records.
Option B: KPL
Aggregates records into larger payloads, significantly improving shard throughput.
Suitable for applications generating small, high-frequency records.
Option C: Kinesis Firehose
Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
Option D: Kinesis SDK
The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
Final Recommendation:
Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.
Reference:
Kinesis Producer Library (KPL) Overview
NEW QUESTION # 101
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?
Answer: B
Explanation:
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost.
Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data.
The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D)would not work, as the data on the instance store volume would be lost if the instance is terminated. References:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2
NEW QUESTION # 102
A data engineer uses AWS Lake Formation to manage access to data that is stored in an Amazon S3 bucket.
The data engineer configures an AWS Glue crawler to discover data at a specific file location in the bucket, s3://examplepath. The crawler execution fails with the following error:
"The S3 location: s3://examplepath is not registered."
The data engineer needs to resolve the error.
Answer: D
Explanation:
Lake Formation requires that S3 locations be registered before they can be accessed by AWS Glue crawlers, jobs, or queries. Registering the location associates it with the Lake Formation permissions framework.
"Before AWS Glue crawlers can access an S3 path managed by Lake Formation, the S3 location must be registered in Lake Formation."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 103
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: B
Explanation:
* Problem Analysis:
* The company uses AWS Glue for ETL pipelines and must enforcedata quality checksduring pipeline execution.
* The goal is to implement quality checks withminimal implementation effort.
* Key Considerations:
* AWS Glue provides anEvaluate Data Quality transformthat allows for defining quality checks directly in the pipeline.
* DQDL (Data Quality Definition Language)simplifies the process by allowing declarative rule definitions.
* Solution Analysis:
* Option A: SQL Transform
* SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
* Option B: Evaluate Data Quality Transform + DQDL
* AWS Glue's built-inEvaluate Data Quality transformis designed for this use case.
* Allows defining thresholds and rules in DQDL with minimal coding effort.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
* Option D: Custom Transform with Great Expectations
* Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
* Final Recommendation:
* Use theEvaluate Data Quality transformwithDQDLto implement data quality rules in AWS Glue pipelines.
:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation
NEW QUESTION # 104
......
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