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

Section	Weight	Objectives
Topic 1: Data Modelling	6%	- Medallion Architecture implementation - Schema design and management - Delta Lake table design
Topic 2: Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching
Topic 3: Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization
Topic 4: Debugging and Deploying	10%	- CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs
Topic 5: Data Governance	7%	- Policy enforcement - Unity Catalog management - Data lineage and metadata tracking
Topic 6: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation
Topic 7: Ensuring Data Security and Compliance	10%	- Access control and permissions - Data encryption and masking - Compliance standards implementation
Topic 8: Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion
Topic 9: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 10: Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?

A. There is a type error because a DataFrame object cannot be multiplied.
B. There is a type error because a column object cannot be multiplied.
C. There is a syntax error because the heartrate column is not correctly identified as a column.
D. There is no column in the table named heartrateheartrateheartrate.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "When using strings directly, Spark SQL interprets them as literal column names." Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate". The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.

NEW QUESTION # 82
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
B. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
D. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
E. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.

Answer: B

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 83
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed.
Which configuration should be set to enable this?

A. checkpointLocation
B. partitionBy
C. mergeSchema
D. overwriteSchema

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates. Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.

NEW QUESTION # 84
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.
B. eturn spark.readStream.load("bronze")
C. return spark.readStream.table("bronze")
D. return spark.read.option("readChangeFeed", "true").table ("bronze")
E.

Answer: E

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

NEW QUESTION # 85
Your team has hundreds of jobs running but it is difficult to track cost of each job run, you are asked to provide a recommendation on how to monitor and track cost across various workloads

A. Use a single cluster for all the jobs, so cost can be easily tracked
B. Use job logs to monitor and track the costs
C. Create jobs in different workspaces, so we can track the cost easily
D. Use Tags, during job creation so cost can be easily tracked
E. Use workspace admin reporting

Answer: D

Explanation:
Explanation
The answer is Use Tags, during job creation so cost can be easily tracked Review below link for more details
https://docs.databricks.com/administration-guide/account-settings/usage-detail-tags-aws.html Here is a view how tags get propagated from pools to clusters and clusters without pools, Diagram Description automatically generated

NEW QUESTION # 86
......

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