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To prepare for the DCPDE exam, candidates should have a solid understanding of data engineering concepts, such as data modeling, data integration, data transformation, and data quality. They should also have experience working with big data technologies, such as Apache Spark, Apache Kafka, and Apache Hadoop.

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is a highly valued industry certification that validates the skills and expertise of data engineers in using Databricks to build and manage data pipelines. Databricks is a cloud-based data platform that offers a unified analytics engine for big data and machine learning. Databricks Certified Professional Data Engineer Exam certification exam is designed to test the candidate's knowledge of Databricks architecture, data engineering best practices, and data pipeline design and implementation.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?

A. def upper_transform(df):
return df.withColumn("value_upper", upper(col("value")))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected)
B. def transform_data(input_df):
# transformation logic here
return output_df
test_input = spark.createDataFrame([(1, "a")], ["id", "value"])
assertDataFrameEqual(transform_data(test_input), expected)
C. def upper_value(df):
return df.withColumn("value_upper", upper(col("value")))
def filter_positive(df):
return df.filter(df["id"] > 0)
pipeline_df = df.transform(upper_value).transform(filter_positive)
D. class Pipeline:
def transform(self, df):
return df.withColumn("value_upper", upper(col("value")))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)

Answer: C

Explanation:
Comprehensive and Detailed
Databricks and Apache Spark recommend building modular and reusable ETL transformations by leveraging the DataFrame.transform() API. This method allows you to chain multiple transformation functions in a clean and testable way.
Option A: Encapsulating the logic in a class (Pipeline) works, but it reduces modularity and flexibility. It does not show the true intended use of DataFrame.transform() which is chaining functional transformations.
Option B: This is the correct approach. It defines small, reusable functions (upper_value, filter_positive) that each take a DataFrame and return a transformed DataFrame. By chaining them with df.transform(func), you can compose ETL pipelines in a clear and declarative manner. This enables unit testing of individual functions and makes the ETL pipeline modular, testable, and production-ready.
Option C: This shows a single transformation wrapped in a function and tested, but it lacks pipeline composition - it is not demonstrating modular chaining across multiple transformations.
Option D: This simply defines a transformation function with hardcoded logic. It does not leverage DataFrame.transform() nor demonstrate modularity through composition.
Therefore, Option B is the best demonstration of how to use DataFrame.transform() in PySpark ETL pipelines.
Databricks documentation explicitly highlights that DataFrame.transform() allows developers to "chain together reusable functions in a readable and modular way, improving testability and maintainability of ETL code." This makes B the correct and officially supported pattern.

NEW QUESTION # 110
Which of the following techniques structured streaming uses to create an end-to-end fault toler-ance?

A. Write ahead logging and idempotent sinks
B. Checkpointing and Water marking
C. Checkpointing and idempotent sinks
D. Stream will failover to available nodes in the cluste
E. Write ahead logging and water marking

Answer: C

Explanation:
Explanation
The answer is Checkpointing and idempotent sinks
How does structured streaming achieves end to end fault tolerance:
*First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
*Next, the streaming sinks are designed to be _idempotent_-that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink.
Taken together, replayable data sources and idempotent sinks allow Structured Streaming to en-sure end-to-end, exactly-once semantics under any failure condition.

NEW QUESTION # 111
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of records is calculated from the parquet file metadata
B. The total count of records is calculated from the Hive metastore
C. The total count of rows is calculated by scanning all data files
D. The total count of records is calculated from the Delta transaction logs
E. The total count of rows will be returned from cached results unless REFRESH is run

Answer: D

Explanation:
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%20number,a

NEW QUESTION # 112
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that
E1 has occurred?

A. P(E2)/P(E1)
B. P(E1+E2)/P(E1)
C. P(E1)/P(E2)
D. P(E2)/(P(E1+E2)

Answer: A

NEW QUESTION # 113
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns . The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB.
How should the data engineer meet this requirement?

A. Apply Z-ordering , since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B. Enable liquid clustering , as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
C. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
D. Use Hive-style partitioning , as it provides efficient data skipping and is easy to change partition columns at any time.

Answer: B

Explanation:
The Databricks documentation describes Liquid Clustering as the recommended data layout optimization for evolving workloads. Unlike traditional partitioning or Z-ordering, Liquid Clustering dynamically maintains data organization without rewriting existing files when clustering keys change. It handles data skew automatically and supports flexible re-clustering based on query patterns. Partitioning and Z-ordering require full data rewrites whenever key structures change, making them expensive for tables with frequently evolving access patterns. For tables under a few terabytes, Liquid Clustering offers the best balance between scalability, adaptability, and maintenance efficiency. Thus, option C aligns with Databricks' best practice for modern adaptive layout optimization.

NEW QUESTION # 114
......

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