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

Section	Weight	Objectives
Topic 1: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Topic 2: Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Optimize and Z-order 3. Time travel and data versioning - Delta Lake operations 1. Delta Live Tables 2. Schema evolution and enforcement 3. Merge, update, delete operations
Topic 3: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Topic 4: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Performance optimization techniques 2. Spark APIs in Python 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions
Topic 5: 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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
Which Python variable contains a list of directories to be searched when trying to locate required modules?

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

Answer: E

NEW QUESTION # 104
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events . Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region.
The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve.

A. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
B. Enable auto-compaction (optimizeWrite and autoCompact).
C. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
D. Enable Delta caching.

Answer: A

Explanation:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next-generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization , Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z-ORDER commands) while improving query performance on large tables-particularly for high-ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low- maintenance solution recommended by Databricks.

NEW QUESTION # 105
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table willrun deduplication over the union of new and existing records, ensuringno duplicate records are present.
B. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
C. Each write to the orders table willonly contain unique records, but newly written records may have duplicates already present in the target table.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
E. Each write to the orders table willonly contain unique records, and only those records without duplicates in the target table will be written.

Answer: C

Explanation:
Explanation
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation usingmergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 106
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

A. The tables should be refreshed in the writing cluster before the next query is run
B. The tables should be stored in a cloud-based external system
C. The tables should be converted to the Delta format
D. The tables should be updated before the next query is run
E. The tables should be altered to include metadata to not cache

Answer: C

NEW QUESTION # 107
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
B. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
C. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

Answer: A

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
Reference:
Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views

NEW QUESTION # 108
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