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

Section	Weight	Objectives
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse
Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. Integration with external systems 2. DBR autoloader 3. Spark APIs for ingestion
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. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions
Delta Lake	20-25%	- Delta Lake operations 1. Schema evolution and enforcement 2. Merge, update, delete operations 3. Delta Live Tables - Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, usingdisplay()calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
B. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
C. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
D. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
E. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

Answer: B

Explanation:
Explanation
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.

NEW QUESTION # 127
The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Configure a job that executes every time new data lands in a given directory.
B. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D. Schedule a job to execute the pipeline once an hour on a new job cluster.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Job clusters are created for a job run and terminate when the job completes." Exact extract: "All-purpose (interactive) clusters are intended for interactive development and collaboration." An hourly batch that completes in ~10 minutes is best run on ephemeral job clusters so compute exists only during execution and auto-terminates afterward, minimizing cost while meeting the hourly SLA. Interactive clusters persist (incurring idle cost). Structured Streaming for an hourly refresh adds continuous compute cost with no benefit here. Event-driven triggers are unnecessary for a simple hourly SLA.
Reference:

NEW QUESTION # 128
You are looking to process the data based on two variables, one to check if the department is supply chain or check if process flag is set to True

A. if department == "supply chain" | if process == TRUE:
B. if department == "supply chain" | process == TRUE:
C. if department == "supply chain" or process = TRUE:
D. if department == "supply chain" or process:
E. if department = "supply chain" | process:

Answer: D

NEW QUESTION # 129
You are tasked to set up a set notebook as a job for six departments and each department can run the task parallelly, the notebook takes an input parameter dept number to process the data by department, how do you go about to setup this up in job?

A. A parameter can only be passed at the job level, create six jobs pass department number to each job with linear job dependency
B. A task accepts key-value pair parameters, creates six tasks pass department number as parameter foreach task with no dependency in the job as they can all run in parallel.
(Correct)
C. A task in the job cannot take an input parameter, create six notebooks with hardcoded dept number and setup six tasks with linear dependency in the job
D. A parameter can only be passed at the job level, create six jobs pass department number to each job with no job dependency
E. Use a single notebook as task in the job and use dbutils.notebook.run to run each note-book with parameter in a different cell

Answer: B

Explanation:
Explanation
Here is how you setup
Create a single job and six tasks with the same notebook and assign a different parameter for each task , Graphical user interface, text, application, email Description automatically generated

All tasks are added in a single job and can run parallel either using single shared cluster or with individual clusters.
Graphical user interface, application, Teams Description automatically generated

NEW QUESTION # 130
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Date
B. User_id
C. Post_time
D. Post_id

Answer: A

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, thedatecolumn is a good candidate for partitioning for several reasons:
* Time-Based Queries: If queries frequently filter or group by date, partitioning by thedatecolumn can significantly improve performance by limiting the amount of data scanned.
* Granularity: Thedatecolumn likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
* Data Skew: Other columns likepost_idoruser_idmight lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning bypost_timecould also be considered, but typicallydateis preferred due to its more manageable granularity.
References:
* Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning

NEW QUESTION # 131
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