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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q144-Q149):

144. Frage
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?

Antwort: B

Begründung:
Problem Analysis:
The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
The goal is to analyze and correlate the two datasets with low operational overhead.
The process must run once nightly.
Key Considerations:
Amazon AppFlow simplifies data integration with Salesforce.
AWS Glue can extract data from MySQL and perform ETL operations.
Step Functions can 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:
Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions


145. Frage
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?

Antwort: D

Begründung:
The RootDiskUsed metric 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 to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.
Reference:
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.
Amazon MSK Storage Auto Scaling


146. Frage
A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

Antwort: C

Begründung:
* Problem Analysis:
* The company usesAWS Gluefor ETL pipelines and requires automaticdata quality checks during pipeline execution.
* The solution must integrate with existing AWS Glue pipelines and evaluatedata quality rules based on predefined thresholds.
* Key Considerations:
* Ensure minimal implementation effort by leveraging built-in AWS Glue features.
* Use a standardized approach for defining and evaluating data quality rules.
* Avoid custom libraries or external frameworks unless absolutely necessary.
* Solution Analysis:
* Option A: SQL Transform
* Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
* Increases operational overhead and deviates from Glue's declarative approach.
* Option B: Evaluate Data Quality Transform with DQDL
* AWS Glue provides a built-inEvaluate Data Quality transform.
* Allows defining rules inData Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
* Fully integrated with Glue Studio, making it the least effort solution.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library for data quality checks but requires custom code and integration.
* Increases implementation effort compared to Glue's native capabilities.
* Option D: Custom Transform with Great Expectations
* Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
* Final Recommendation:
* UseEvaluate Data Quality transformin AWS Glue.
* Define rules inDQDLfor checking thresholds, null values, or other quality criteria.
* This approach minimizes development effort and ensures seamless integration with AWS Glue.
:
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations


147. Frage
An ecommerce company stores sales data in an AWS Glue table named sales_data. The company stores the sales_data table in an Amazon S3 Standard bucket. The table contains columns named order_id, customer_id, product_id, order_date, shipping_date, and order_amount.
The company wants to improve query performance by partitioning the sales_data table by order_date. The company needs to add the partition to the existing sales_data table in AWS Glue.
Which solution will meet these requirements?

Antwort: D

Begründung:
In AWS Glue, table partitions are managed as metadata objects within the AWS Glue Data Catalog. To add a new partition to an existing table, the correct and supported approach is to use the AWS Glue Data Catalog API, such as the CreatePartition operation, or equivalent console or SDK actions.
Updating the table schema alone does not create partitions or inform Glue about new partition values. Editing metadata files directly in Amazon S3 is unsupported and can corrupt the Data Catalog. Manually modifying the S3 bucket structure without registering partitions in Glue will result in Athena and other query engines being unable to recognize the partitions.
By adding partitions through the Glue Data Catalog API, query engines such as Amazon Athena and Amazon Redshift Spectrum can perform partition pruning, which significantly improves query performance by scanning only relevant data.
This method aligns with AWS best practices, ensures metadata consistency, and avoids unnecessary operational risk. Therefore, Option C is the correct solution.


148. Frage
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.

Antwort: B

Begründung:
For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.
Athena scales automatically and doesn't degrade with increasing data size when partitions are managed efficiently.
"Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This is the most cost-effective and scalable option for date-based queries.


149. Frage
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