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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Store Management	26%	- Understand data cataloging 1. Data discovery and classification 2. AWS Glue Data Catalog 3. Schema evolution - Design data models 1. Normalization and denormalization 2. Schema design 3. Partitioning and indexing strategies - Choose a data store 1. Data lakes vs. data warehouses 2. Data characteristics (structured, semi-structured, unstructured) 3. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift 4. Access and storage patterns - Manage data lifecycle 1. Data archiving 2. Data retention policies 3. Amazon S3 storage classes
Topic 2: Data Operations and Support	22%	- Manage and troubleshoot data processes 1. Cost optimization 2. Performance tuning 3. Debugging failed jobs - Automate data pipelines 1. Scheduling jobs 2. Event-driven triggers 3. AWS Lambda triggers - Monitor data pipelines 1. Amazon CloudWatch 2. Logging and metrics 3. AWS CloudTrail
Topic 3: Data Security and Governance	18%	- Ensure data encryption 1. Encryption at rest and in transit 2. AWS KMS - Apply authentication and authorization 1. Amazon S3 bucket policies 2. AWS IAM policies and roles 3. Service control policies (SCPs) - Implement data quality checks 1. Data validation 2. AWS Glue DataBrew - Manage data privacy and compliance 1. AWS Lake Formation permissions 2. Data masking and tokenization 3. PII data handling
Topic 4: Data Ingestion and Transformation	34%	- Apply programming concepts 1. SQL, Python, Scala 2. Infrastructure as Code (IaC) 3. Version control - Perform data ingestion 1. Data ingestion patterns (frequency and data history) 2. Throughput and latency characteristics for AWS services 3. Batch data ingestion (scheduled ingestion, event-driven ingestion) 4. Replayability of data 5. Streaming data ingestion - Transform and process data 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) 2. Batch and stream processing 3. Data partitioning and compression 4. ETL/ELT patterns - Orchestrate data pipelines 1. Amazon Managed Workflows for Apache Airflow (MWAA) 2. AWS Glue Workflows 3. Event-driven architectures 4. AWS Step Functions

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q92-Q97):

NEW QUESTION # 92
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
B. Use an S3 bucket that is in the same account that uses Athena to query the data.
C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
D. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
E. Preprocess the .csvdata to JSON format by fetchingonly the document keys that the query requires.

Answer: A,C

Explanation:
Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted.
Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row-based and verbose formats that require more data scanning and fetching than columnar formats like Parquet. References:
Best Practices When Using Athena with AWS Glue
Optimizing Amazon S3 Performance
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

NEW QUESTION # 93
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?

A. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.
B. Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
C. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data.Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.
D. Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

Answer: D

Explanation:
AWS Glue DataBrew provides a no-code data preparation and validation interface. It allows you to set data profiling, completeness checks, and numeric range validations directly through its UI-ideal for IoT validation use cases.
"AWS Glue DataBrew enables users to define validation rules such as completeness and value range checks without writing code."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This fulfills the requirement for minimal coding and automatic data quality flagging.

NEW QUESTION # 94
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
B. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
C. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team-specific alerts.
D. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors. Allow each team to poll their respective SQS queue for relevant errors.

Answer: C

Explanation:
Option B is the best answer because Amazon EventBridge is designed to route events based on matching rules, and each rule can use a specific event pattern to match only the relevant error type and then send that event to the correct target. AWS states that EventBridge event buses receive events and deliver them to targets, and that rules can match on event details and route matching events accordingly. This makes it ideal for real-time error classification with very little custom code. An SNS topic per team then provides immediate push-based notification delivery.
Option A adds unnecessary operational overhead because Lambda would be doing routing logic that EventBridge rules already handle natively. Option C is weaker because SQS is pull-based; teams must poll queues, which is not the best fit for "without any delay" notifications. AWS documentation explicitly describes SQS consumption through polling. Option D is also not the best fit because CloudWatch alarms are threshold-based metric monitors, not event routers for per-message error classification.
This aligns with the study guide's emphasis on using managed AWS services to automate and orchestrate pipeline actions with the least overhead.

NEW QUESTION # 95
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?

A. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: A

Explanation:
Problem Analysis:
The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.
The goal is to implement quality checks with minimal implementation effort.
Key Considerations:
AWS Glue provides an Evaluate Data Quality transform that 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-in Evaluate Data Quality transform is 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 the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.
Reference:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation

NEW QUESTION # 96
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
B. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
C. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
D. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

Answer: D

Explanation:
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
* Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3)
* Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes)
* Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)

NEW QUESTION # 97
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