Data-Engineer-Associate Schulungsunterlagen, Data-Engineer-Associate Antworten

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q118-Q123):

118. Frage
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)

Antwort: B,D

Begründung:
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.


119. Frage
A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day.
The company wants to control user access to the objects based on each user's job role, permissions, andhow sensitive the data is.
Which solution will meet these requirements?

Antwort: C

Begründung:
Amazon Redshift supportsRole-Based Access Control (RBAC)to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.
"RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.


120. Frage
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

Antwort: B

Begründung:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:
Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore) Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog) AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)


121. Frage
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

Antwort: C

Begründung:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine ora Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3


122. Frage
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?

Antwort: D

Begründung:
The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.
* Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.
* Option B (AWS Step Functions) can orchestrate tasks but doesn't natively support complex workflow definitions with Python like Airflow does.
* Option C (AWS Glue) is more focused on ETL and doesn't handle the orchestration of external systems like Salesforce as well as Airflow.
* Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.
References:
Amazon Managed Workflows for Apache Airflow
Apache Airflow on AWS


123. Frage
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