Test Data-Engineer-Associate Preparation - Original Data-Engineer-Associate Questions

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Data Operations and Support22%- Automate data pipelines
  • 1. AWS Lambda triggers
  • 2. Event-driven triggers
  • 3. Scheduling jobs
- Manage and troubleshoot data processes
  • 1. Debugging failed jobs
  • 2. Cost optimization
  • 3. Performance tuning
- Monitor data pipelines
  • 1. Logging and metrics
  • 2. AWS CloudTrail
  • 3. Amazon CloudWatch
Data Store Management26%- Design data models
  • 1. Normalization and denormalization
  • 2. Schema design
  • 3. Partitioning and indexing strategies
- Understand data cataloging
  • 1. Schema evolution
  • 2. AWS Glue Data Catalog
  • 3. Data discovery and classification
- Manage data lifecycle
  • 1. Data retention policies
  • 2. Data archiving
  • 3. Amazon S3 storage classes
- Choose a data store
  • 1. Data characteristics (structured, semi-structured, unstructured)
  • 2. Data lakes vs. data warehouses
  • 3. Access and storage patterns
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
Data Ingestion and Transformation34%- Orchestrate data pipelines
  • 1. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 2. AWS Step Functions
  • 3. Event-driven architectures
  • 4. AWS Glue Workflows
- Apply programming concepts
  • 1. Infrastructure as Code (IaC)
  • 2. Version control
  • 3. SQL, Python, Scala
- Perform data ingestion
  • 1. Data ingestion patterns (frequency and data history)
  • 2. Replayability of data
  • 3. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 4. Streaming data ingestion
  • 5. Throughput and latency characteristics for AWS services
- Transform and process data
  • 1. ETL/ELT patterns
  • 2. Batch and stream processing
  • 3. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 4. Data partitioning and compression
Data Security and Governance18%- Manage data privacy and compliance
  • 1. PII data handling
  • 2. Data masking and tokenization
  • 3. AWS Lake Formation permissions
- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
- Implement data quality checks
  • 1. Data validation
  • 2. AWS Glue DataBrew
- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. AWS IAM policies and roles
  • 3. Amazon S3 bucket policies

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Original Data-Engineer-Associate Questions & Data-Engineer-Associate 100% Correct Answers

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q303-Q308):

NEW QUESTION # 303
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
* Problem Analysis:
* The company processes2 GB of daily sales recordsand100 GB of Salesforce sales opportunities
.
* The goal is to analyze and correlate the two datasets withlow operational overhead.
* The process must runonce nightly.
* Key Considerations:
* Amazon AppFlowsimplifies data integration with Salesforce.
* AWS Gluecan extract data from MySQL and perform ETL operations.
* Step Functionscan orchestrate workflows with minimal manual intervention.
* Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
* Solution Analysis:
* Option A: MWAA + Lambda + Step Functions
* Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
* Option B: AppFlow + Glue + MWAA
* MWAA adds orchestration overhead compared to the simpler Step Functions.
* Option C: AppFlow + Glue + Step Functions
* AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
* Minimal setup and operational overhead, making it the best choice.
* Option D: AppFlow + Kinesis + Flink + Step Functions
* Using Kinesis and Flink for batch processing introduces unnecessary complexity.
* Final Recommendation:
* UseAmazon AppFlowto fetch Salesforce data,AWS Glueto process MySQL data, andStep Functionsfor orchestration.
:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions


NEW QUESTION # 304
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real- time streaming data. The data in the Iceberg tables is stored in Apache Parquet format. The company is experiencing slow query performance.
Which solutions will improve query performance? (Select TWO)

Answer: A,B

Explanation:
Apache Iceberg query performance depends heavily on metadata optimization and file layout efficiency.
Generating column-level statistics allows query engines such as AWS Glue, Amazon Athena, and Amazon Redshift to perform predicate pushdown and data skipping. These statistics help avoid scanning unnecessary Parquet files and significantly reduce query execution time.
Real-time streaming workloads often generate many small files, which degrades query performance. Table compaction consolidates small Parquet files into fewer, larger files, improving scan efficiency and reducing metadata overhead. Iceberg supports compaction as a core optimization technique, and Glue can schedule these operations automatically.
Iceberg does not use traditional indexes, so index optimization is not applicable. Copy-on-write affects write semantics and consistency, not query speed. Views do not improve physical query performance.
Therefore, generating statistics and compacting files are the two most effective optimizations.


NEW QUESTION # 305
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: C

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 # 306
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

Answer: D

Explanation:
TheRootDiskUsedmetric for the MSK cluster indicates that the storage on the broker is reaching its capacity.
The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
* Expand MSK Broker Storage:
* AWS Managed Streaming for Apache Kafka (MSK) allows you toexpand the broker storageto accommodate growing data volumes. Additionally,auto-expansionof storage can be configured to ensure that storage grows automatically as the data increases.
Reference:Amazon MSK Cluster Storage Expansion
Alternatives Considered:
B (Expand Zookeeper storage): Zookeeper is responsible for managing Kafka metadata and not for storing data, so increasing Zookeeper storage won't resolve the root disk issue.
C (Update instance type): Changing the instance type would increase computational resources but not directly address the storage problem.
D (Target-Volume-in-GiB): This parameter is irrelevant for the existing topic and will not solve the storage issue.
References:
Amazon MSK Storage Auto Scaling


NEW QUESTION # 307
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

Answer: A

Explanation:
The requirement involves transforming CSV files by renaming columns, removing rows, and other operations with minimal development effort. AWS Glue DataBrew is the best solution here because it allows you to visually create transformation recipes without writing extensive code.
* Option D: Use AWS Glue DataBrew recipes to read and transform the CSV files.DataBrew provides a visual interface where you can build transformation steps (e.g., renaming columns, filtering rows, creating new columns, etc.) as a "recipe" that can be applied to datasets, making it easy to handle complex transformations on CSV files with minimal coding.
Other options (A, B, C) involve more manual development and configuration effort (e.g., writing Python jobs or creating custom workflows in Glue) compared to the low-code/no-code approach of DataBrew.
References:
* AWS Glue DataBrew Documentation


NEW QUESTION # 308
......

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