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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. Differences between data lake, data warehouse, and lakehouse 2. Bronze, silver, gold data layers 3. Data governance fundamentals
Topic 2: 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. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables
Topic 3: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Built-in and user-defined functions 3. Performance optimization techniques - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions
Topic 4: Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems
Topic 5: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

Answer: D

Explanation:
Explanation
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html

NEW QUESTION # 31
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires
C. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

Answer: C

Explanation:
Setting multiple fields in a single update.
Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.
Reference:
Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake

NEW QUESTION # 32
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables.
Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
B. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
C. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
D. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
E. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

Answer: D

Explanation:
Explanation
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.

NEW QUESTION # 33
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 TBLPROPERTIES (del-ta.deletedFileRetentionDuration= 'interval 365 days')
C. ALTER TABLE table_name set EXENDED TBLPROPERTIES (delta.vaccum.duration= 'interval 365 days')
D. ALTER TABLE table_name set EXENDED TBLPROPERTIES (del-ta.deletedFileRetentionDuration=
'interval 365 days')

Answer: B

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 # 34
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create .

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. Three new jobs named " Ingest new data " will be defined in the workspace, but no jobs will be executed.
B. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
C. Three new jobs named " Ingest new data " will be defined in the workspace, and they will each run once daily.
D. One new job named " Ingest new data " will be defined in the workspace, but it will not be executed.
E. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Answer: A

Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring and Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 35
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