Databricks-Certified-Data-Engineer-Associate Zertifizierungsprüfung & Databricks-Certified-Data-Engineer-Associate Exam Fragen

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

Section	Weight	Objectives
Topic 1: Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts
Topic 2: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs)
Topic 3: Delta Lake Fundamentals	20-25%	- Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables - Understand ACID transactions and time travel
Topic 4: Data Pipeline Architecture	15-20%	- Design data pipelines for batch and streaming - Implement incremental data processing - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance
Topic 5: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries - Aggregate and group data
Topic 6: Apache Spark Data Processing Fundamentals	20-25%	- Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs)

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q195-Q200):

195. Frage
In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?

A. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
B. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL)
C. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
D. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)

Antwort: A

Begründung:
ON VIOLATION DROP ROW removes failing records from the target dataset and records the violation in the event log while the pipeline continues. An expectation with no ON VIOLATION clause retains bad records and only reports metrics, and FAIL UPDATE stops the pipeline.

196. Frage
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. There is no way to indicate whether a table contains PII.
B. "COMMENT PII"
C. COMMENT "Contains PII"
D. TBLPROPERTIES PII
E. PII

Antwort: C

Begründung:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. Reference: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

197. Frage
A data engineer must apply a daily batch of customer records to a Delta table so that existing customers are updated, new customers are inserted, and no duplicates are created.
Which statement accomplishes this in a single atomic operation?

A. INSERT OVERWRITE TABLE customers SELECT * FROM updates
B. MERGE INTO customers t USING updates s ON t.customer_id = s.customer_id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *
C. CREATE OR REPLACE TABLE customers AS SELECT * FROM updates
D. INSERT INTO customers SELECT * FROM updates

Antwort: B

Begründung:
MERGE INTO performs upserts in a single ACID transaction, updating matched rows and inserting unmatched rows. INSERT INTO would create duplicates, and both INSERT OVERWRITE and CREATE OR REPLACE would discard existing records not present in the daily batch.

198. Frage
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

A. A lightweight single-node cluster with low worker node count to reduce costs.
B. A high-concurrency cluster designed for interactive SQL workloads.
C. A job cluster configured to autoscale across multiple workers during the pipeline run.
D. An all-purpose cluster always kept running to ensure low-latency job startup times.

Antwort: C

Begründung:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.
Reference Source: Databricks Compute and Job Cluster Configuration Documentation - "Autoscaling and Job Clusters."

199. Frage
A data engineer wants to delete records from a Delta table where the status column equals
"inactive". The operation should modify only the affected rows without rewriting the entire dataset.
Which SQL command should be used?

A. UPDATE
B. MERGE
C. INSERT
D. DELETE

Antwort: D

200. Frage
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