Databricks-Certified-Data-Engineer-Associate勉強資料 & Databricks-Certified-Data-Engineer-Associate的中問題集

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

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

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Databricks-Certified-Data-Engineer-Associate的中問題集、Databricks-Certified-Data-Engineer-Associate参考書内容

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q132-Q137):

質問 # 132
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation cause the job to fail.
B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

正解：B

解説：
Explanation
With the defined constraint and expectation clause, when a batch of data is processed, any records that violate the expectation (in this case, where the timestamp is not greater than '2020-01-01') will be dropped from the target dataset. These dropped records will also be recorded as invalid in the event log, allowing for auditing and tracking of the data quality issues without causing the entire job to fail.
https://docs.databricks.com/en/delta-live-tables/expectations.html

質問 # 133
A data engineer is troubleshooting a Lakeflow Spark Declarative Pipelines (SDP) where several rows are dropped by expectations. The product owner requests details on which expectation failed most frequently during the last run and the corresponding affected table. The engineer wants to use Databricks' built-in observability data to answer this without adding new logging code. How should the data engineer troubleshoot SDP to verify the expectation failure?

A. Increase the cluster's log level to DEBUG and parse the driver's Log4j logs for expectation metrics for the last run.
B. Query the Lakeflow Spark Declarative Pipelines event log to retrieve expectation metrics and failure details for the pipeline's most recent update.
C. Export notebook run results to HTML and search for the expectations summary in the rendered output to compile the metrics.
D. Use the Spark UI's SQL tab to identify failed rows and expectation names from physical query plans.

正解：B

解説：
Lakeflow Spark Declarative Pipelines automatically record expectation metrics and outcomes in the pipeline event log, which can be queried to identify failure counts and affected tables without adding custom logging.

質問 # 134
A data engineering team is designing the Gold layer in their Unity Catalog-governed lakehouse for downstream BI and analytics users. The team wants to expose business-ready metrics with fast query performance and consistent definitions, while keeping the transformation logic in Spark notebooks. Which type of Gold layer object meets this requirement?

A. A streaming table that continuously ingests raw events from source systems and exposes them directly to BI users.
B. A materialized view that precomputes aggregations from Silver tables on a schedule and is queried by BI tools for faster, consistent analytics.
C. A collection of CSV files stored in a cloud storage object. BI users will parse the files to query the gold layer data.
D. A view that directly references raw Bronze tables so BI tools can define their own joins and aggregations.

正解：B

解説：
A materialized view stores precomputed Gold-layer aggregations, providing BI tools with fast queries and centrally defined, consistent business results while allowing the transformation logic to remain in Databricks notebooks.

質問 # 135
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

A. if day_of_week = 1 and review_period:
B. if day_of_week == 1 and review_period:
C. if day_of_week = 1 and review_period = "True":
D. if day_of_week = 1 & review_period: = "True":
E. if day_of_week == 1 and review_period == "True":

正解：B

解説：
In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. References: [Python Operators], [Python If ... Else]

質問 # 136
A data engineer needs Task C to run ONLY if Task A succeeds AND Task B fails. Which dependency configuration implements this conditional logic?

A. Task C uses two dependency chains: Task A with All succeeded, Task B with at least one failed.
B. Task C depends on Task A (All done) and Task B (All done) with a condition widget.
C. Create a Python task that checks dbutils.jobs.taskValues from Tasks A and B before running Task C.
D. Task C depends on Task A (Success) with Task B listed as a negative dependency.

正解：A

解説：
Use separate gate tasks: one runs only when Task A succeeds, and another runs only when Task B fails. Task C then depends on both gates with All succeeded, enforcing the exact A succeeded AND B failed condition. A single Run if setting applies collectively to all dependencies and cannot express mixed per-task outcomes.

質問 # 137
......

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Databricks-Certified-Data-Engineer-Associate的中問題集: https://www.jpexam.com/Databricks-Certified-Data-Engineer-Associate_exam.html

P.S.JpexamがGoogle Driveで共有している無料の2026 Databricks Databricks-Certified-Data-Engineer-Associateダンプ：https://drive.google.com/open?id=1Wo8Cp990w83gO9sx-Q6c6tXB6cs0_t3D