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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
How are Delt tables stored?

A. A Directory where parquet data files are stored, all of the metadata is stored in parquet files
B. Data is stored in Data plane and Metadata and delta log are stored in control pane
C. A Directory where parquet data files are stored, a sub directory _delta_log where meta data, and the transaction log is stored as JSON files.
D. A Directory where parquet data files are stored, all of the meta data is stored in memory
E. A Directory where parquet data files are stored in Data plane, a sub directory _delta_log where meta data, history and log is stored in control pane.

Answer: C

Explanation:
Explanation
The answer is A Directory where parquet data files are stored, a sub directory _delta_log where meta data, and the transaction log is stored as JSON files.
Timeline Description automatically generated

NEW QUESTION # 119
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
E. Both commands will fail. No new variables, tables, or views will be created.

Answer: D

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 120
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
C. The update throws an error because changes to existing columns in the target schema are not supported.
D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

Answer: A

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
Reference:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.html#schema-evolution

NEW QUESTION # 121
You were asked to setup a new all-purpose cluster, but the cluster is unable to start which of the following steps do you need to take to identify the root cause of the issue and the reason why the cluster was unable to start?

A. Workspace logs
B. Storage account
C. Check the cluster event logs
(Correct)
D. Data plane
E. Check the cluster driver logs

Answer: C

Explanation:
Explanation
Cluster event logs are very useful, to identify issues pertaining to cluster availability. Cluster may not start due to resource limitations or issues with the cloud providers.
Some of the common issues include a subnet for compute VM reaching its limits or exceeding the subscription or cloud account CPU quota limit.
Here is an example where the cluster did not start due to subscription reaching the quota limit on a certain type of cpu cores for a VM type.
Graphical user interface, text, application, email Description automatically generated

Click on event logs
Graphical user interface, text, application, email Description automatically generated

Click on the message to see the detailed error message on why the cluster did not start.
Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 122
A data engineer is creating a daily reporting job. There are two reporting notebooks-one for weekdays and one for weekends. An "if/else condition" task is configured as {{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones.
Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
B. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
C. Merge, as the job configuration looks good.
D. Reject, as they should use {{job.trigger_time.is_weekday}} instead.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

NEW QUESTION # 123
......

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