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

Section	Weight	Objectives
Topic 1: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Topic 2: Data Processing	28%	- Structured Streaming - Data Transformation - ETL Pipelines - Spark SQL
Topic 3: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Topic 4: Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance
Topic 5: Databricks Lakehouse Platform	24%	- Unity Catalog - Data Management - Lakehouse Architecture - Delta Lake

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
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Choose the response that correctly fills in the blank within the code block to complete this task.

A. "event_time"
B. window("event_time", "5 minutes").alias("time")
C. to_interval("event_time", "5 minutes").alias("time")
D. lag("event_time", "10 minutes").alias("time")
E. window("event_time", "10 minutes").alias("time")

Answer: B

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 28
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
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A. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
B. Parse the Delta Lake transaction log to identify all newly written data files.
C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.

Answer: A

Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

NEW QUESTION # 29
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
C. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
D. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

Answer: A

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

NEW QUESTION # 30
Which Python variable contains a list of directories to be searched when trying to locate required modules?

A. pypi.path
B. pylib.source
C. importlib.resource path
D. sys.path
E. os-path

Answer: D

Explanation:
sys. path is a built-in variable within the sys module. It contains a list of directories that the interpreter will search in for the required module.

NEW QUESTION # 31
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
B. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
C. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
D. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.

Answer: C

Explanation:
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.

NEW QUESTION # 32
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