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

Section	Weight	Objectives
Delta Lake	20-25%	- Delta Lake operations 1. Delta Live Tables 2. Merge, update, delete operations 3. Schema evolution and enforcement - Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions
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. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Built-in and user-defined functions 3. Performance optimization techniques

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
B. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
C. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Answer: D

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 214
Which statement describes the default execution mode for Databricks Auto Loader?

A. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
B. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
C. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

Answer: B

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake. The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.
References:
* Databricks Auto Loader Documentation: Auto Loader Guide
* Delta Lake and Auto Loader: Delta Lake Integration

NEW QUESTION # 215
John Smith is a newly joined team member in the Marketing team who currently has access read access to sales tables but does not have access to delete rows from the table, which of the following commands help you accomplish this?

A. GRANT DELETE TO TABLE table_name ON john.smith@marketing.com
B. GRANT USAGE ON TABLE table_name TO john.smith@marketing.com
C. GRANT MODIFY TO TABLE table_name ON john.smith@marketing.com
D. GRANT DELETE ON TABLE table_name TO john.smith@marketing.com
E. GRANT MODIFY ON TABLE table_name TO john.smith@marketing.com

Answer: E

Explanation:
Explanation
The answer is GRANT MODIFY ON TABLE table_name TO john.smith@marketing.com , please note INSERT, UPDATE, and DELETE are combined into one role called MODIFY.
Below are the list of privileges that can be granted to a user or a group, SELECT: gives read access to an object.
CREATE: gives the ability to create an object (for example, a table in a schema).
MODIFY: gives the ability to add, delete, and modify data to or from an object.
USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.
READ_METADATA: gives the ability to view an object and its metadata.
CREATE_NAMED_FUNCTION: gives the ability to create a named UDF in an existing catalog or schema.
MODIFY_CLASSPATH: gives the ability to add files to the Spark classpath.
ALL PRIVILEGES: gives all privileges (is translated into all the above privileges

NEW QUESTION # 216
A table named user_ltv is being used to create a view that will be used by data analysis on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
B. All records from all columns will be displayed with the values in user_ltv.
C. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
D. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

Answer: D

Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 217
You currently working with the marketing team to setup a dashboard for ad campaign analysis, since the team is not sure how often the dashboard should be refreshed they have decided to do a manual refresh on an as needed basis. Which of the following steps can be taken to reduce the overall cost of the compute when the team is not using the compute?
*Please note that Databricks recently change the name of SQL Endpoint to SQL Warehouses.

A. They can turn on the Auto Stop feature for the SQL endpoint(SQL Warehouse).
B. They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse).
C. They can decrease the maximum bound of the SQL endpoint(SQL Warehouse) scaling range.
D. They can decrease the cluster size of the SQL endpoint(SQL Warehouse).
E. They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse) and change the Spot Instance Policy from "Reliability Optimized" to "Cost optimized"

Answer: A

Explanation:
Explanation
The answer is, They can turn on the Auto Stop feature for the SQL endpoint(SQL Warehouse).
Use auto stop to automatically terminate the cluster when you are not using it.

NEW QUESTION # 218
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