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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
E. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

Answer: D

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 121
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, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
C. 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.
D. 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.
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: C

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 # 122
The default threshold of VACUUM is 7 days, internal audit team asked to certain tables to maintain at least
365 days as part of compliance requirement, which of the below setting is needed to implement.

A. MODIFY TABLE table_name set TBLPROPERTY (delta.maxRetentionDays = 'inter-val 365 days')
B. ALTER TABLE table_name set EXENDED TBLPROPERTIES (del-ta.deletedFileRetentionDuration=
'interval 365 days')
C. ALTER TABLE table_name set TBLPROPERTIES (del-ta.deletedFileRetentionDuration= 'interval 365 days')
D. ALTER TABLE table_name set EXENDED TBLPROPERTIES (delta.vaccum.duration= 'interval 365 days')

Answer: C

Explanation:
Explanation
1.ALTER TABLE table_name SET TBLPROPERTIES ( property_key [ = ] property_val [, ...] ) TBLPROPERTIES allow you to set key-value pairs Table properties and table options (Databricks SQL) | Databricks on AWS

NEW QUESTION # 123
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity.
Which SQL query should the team run from the System catalog to achieve this?
A)
SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B)
SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
C)
SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D)
SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

A. Option E
B. Option D
C. Option A
D. Option B
E. Option C

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non-existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

NEW QUESTION # 124
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. * Total VMs: 8
* 50 GB per Executor
* 20 Cores / Executor
B. * Total VMs; 1
* 400 GB per Executor
* 160 Cores / Executor

Answer: A

Explanation:
C.
* Total VMs: 4
* 100 GB per Executor
* 40 Cores/Executor
D.
* Total VMs:2
* 200 GB per Executor
* 80 Cores / Executor
Explanation:
This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance the trade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block. Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Cluster configurations" section.

NEW QUESTION # 125
......

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