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

SectionWeightObjectives
Topic 1: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Differences between data lake, data warehouse, and lakehouse
  • 2. Data governance fundamentals
  • 3. Bronze, silver, gold data layers
Topic 2: Delta Lake20-25%- Delta Lake fundamentals
  • 1. Time travel and data versioning
  • 2. ACID transactions
  • 3. Optimize and Z-order
- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Merge, update, delete operations
  • 3. Delta Live Tables
Topic 3: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Monitoring and alerting
  • 3. Jobs and job scheduling
Topic 4: Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader
Topic 5: Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Spark APIs in Python
  • 2. Built-in and user-defined functions
  • 3. Performance optimization techniques
- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format("parquet")
.load("/mnt/raw_orders/")
.withWatermark("time", "2 hours")
.dropDuplicates(["customer_id", "order_id"])
.writeStream
.trigger(once=True)
.table("orders")
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay." Exact extract: "Records older than the event-time watermark are considered late and may be dropped." Exact extract: "trigger(once) processes all available data once and then stops."


NEW QUESTION # 46
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

Answer: E

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.


NEW QUESTION # 47
A table named user_ltv is being used to create a view that will be used by data analysis on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

Answer: A

Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.


NEW QUESTION # 48
You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

Answer: B

Explanation:
Explanation
The answer is Databricks Repos allow you to add comments and select the changes you want to commit.


NEW QUESTION # 49
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?

Answer: C

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.
Reference: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).


NEW QUESTION # 50
......

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