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

Section	Weight	Objectives
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Task dependencies and orchestration 3. Jobs and job scheduling
Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader
Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Time travel and data versioning 3. Optimize and Z-order - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Data governance fundamentals 3. Bronze, silver, gold data layers
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming ?

A. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
D. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

Answer: D

Explanation:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A - automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 99
Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

A. All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.
B. All of the data and log are stored in a single parquet file
C. All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.
D. All of the data is stored into one parquet file, log files are broken down into one or many json files.
E. All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
(Correct)

Answer: E

Explanation:
Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,

NEW QUESTION # 100
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
C. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Answer: D

Explanation:
Explanation
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under
"CREATE VIEW" section.

NEW QUESTION # 101
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. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. delayWrite("event_time", "10 minutes")
B. slidingWindow("event_time", "10 minutes")
C. awaitArrival("event_time", "10 minutes")
D. withWatermark("event_time", "10 minutes")
E. await("event_time + '10 minutes'")

Answer: D

Explanation:
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

NEW QUESTION # 102
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A. return spark.read.option("readChangeFeed", "true").table ("bronze")
B. return spark.readStream.load("bronze")
C.
D. return spark.readStream.table("bronze")
E.

Answer: E

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 103
......

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