Data-Engineer-Associate Fragenkatalog, Data-Engineer-Associate Deutsche

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

>> Data-Engineer-Associate Fragenkatalog <<

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q61-Q66):

61. Frage
A company receives .csv files that contain physical address dat
a. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

Antwort: C

Begründung:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. Reference:
7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns) NEST TO MAP - AWS Glue DataBrew (Section: Syntax)


62. Frage
A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.
Which SQL statement should the data engineer use to meet this requirement?

Antwort: D

Begründung:
* Problem Analysis:
* The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).
* The solution must avoid copying any data.
* Key Considerations:
* CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.
* Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.
* Solution Analysis:
* Option A: Copies both schema and data. Does not meet the requirement for an empty table.
* Option B: Inserts data into an existing table, which does not create a new table.
* Option C: Creates an empty table but does not copy the schema.
* Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.
* Final Recommendation:
* Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.
Athena CTAS Queries
CREATE TABLE Statement in Athena


63. Frage
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

Antwort: C

Begründung:
AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. Reference:
AWS Glue
FindMatches ML Transform
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


64. Frage
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?

Antwort:

Begründung:
Inspect the job run monitoring section of the AWS Glue console.
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.


65. Frage
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)

Antwort: B,C

Begründung:
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
* Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
* Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
* SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
* Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
References:
* Amazon Neptune Documentation
* Gremlin Documentation
* SPARQL Documentation


66. Frage
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