Databricks-Certified-Data-Engineer-Associate Fragen Antworten - Databricks-Certified-Data-Engineer-Associate Antworten

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Compute options: clusters, SQL warehouses, serverless
  • 2. Data layout and optimization: partitioning, file sizing, caching
  • 3. Workspace navigation and management
Data Governance & Quality11%- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control
Productionizing Data Pipelines18%- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
- Monitoring and troubleshooting
  • 1. Logging and error handling
  • 2. Pipeline reliability and recovery
- Deployment and CI/CD
  • 1. Version control integration
  • 2. Databricks Asset Bundles
Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Batch and streaming ingestion
  • 3. Delta Lake basics and usage
- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL
Data Processing & Transformations31%- Data transformation techniques
  • 1. Complex data processing logic
  • 2. Aggregations, joins, and window functions
  • 3. DataFrame operations and transformations
- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q268-Q273):

268. Frage
Which of the following describes the relationship between Bronze tables and raw data?

Antwort: D


269. Frage
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
A data engineer has created an ETL pipeline using Delta Live table to manage their company travel reimbursement detail, they want to ensure that the if the location details has not been provided by the employee, the pipeline needs to be terminated.
How can the scenario be implemented?

Antwort: B

Begründung:
In the given scenario, the data engineer wants to ensure that if the location details have not been provided by the employee, the ETL pipeline should be terminated. This means that the data engineer needs a strict enforcement of the constraint, and the pipeline should fail if there is a violation. The correct option for this is:
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE: This will ensure that if the location is NULL, the pipeline will fail the update, effectively terminating the process as required.
The other options are incorrect because:
CONSTRAINT valid_location EXPECT (location = NULL): This is incorrect because it expects the value to be NULL, which is the opposite of what is intended here.
CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW): This would drop rows that violate the constraint, but it would not terminate the pipeline, which does not meet the requirement.
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL: This would terminate the pipeline, but the correct syntax is ON VIOLATION FAIL UPDATE, which clearly indicates how to handle updates.


270. Frage
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

Antwort: C


271. Frage
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

Antwort: E

Begründung:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics


272. Frage
An organization needs to share a dataset stored in its Databricks Unity Catalog with an external partner who uses a different data platform that is not Databricks. The goal is to maintain data security and ensure the partner can access the data efficiently.
Which method should the data engineer use to securely share the dataset with the external partner?

Antwort: A


273. Frage
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