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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for professionals who want to demonstrate their expertise in using Databricks to manage big data and create data pipelines. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers, data architects, data scientists, and other professionals who work with big data and want to validate their skills in using Databricks to build data pipelines.

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Databricks Certified Professional Data Engineer exam measures a candidate's ability to design, build, and manage data pipelines using Databricks. It covers a wide range of topics, including data ingestion, transformation, storage, and analysis. Candidates must demonstrate their proficiency in using Databricks tools and techniques to solve real-world data engineering problems. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers who want to validate their skills and expertise in using Databricks to build and manage data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?

A. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
B. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
C. Databricks Repos can commit or push code changes to trigger a CI/CD process
D. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
E. Databricks Repos can store the single-source-of-truth Git repository

Answer: C

NEW QUESTION # 153
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
C. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

Answer: B

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 154
Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql("select count(*) from table").collect()[0][0]

A. assert (row_count = 10, "Row count did not match")
B. assert if (row_count = 10, "Row count did not match")
C. assert if row_count == 10, "Row count did not match"
D. assert row_count == 10, "Row count did not match"
E. assert row_count = 10, "Row count did not match"

Answer: D

Explanation:
Explanation
The answer is assert row_count == 10, "Row count did not match"
Review below documentation

NEW QUESTION # 155
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 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)
C. 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)
D. class Pipeline:
def transform(self, df):
return df.withColumn("value_upper", upper(col("value")))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)

Answer: B

Explanation:
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 # 156
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Configure a job that executes every time new data lands in a given directory.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Schedule a job to execute the pipeline once hour on a new job cluster.
D. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

Answer: C

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost- effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html

NEW QUESTION # 157
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