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Databricks Certified Professional Data Engineer exam is designed to test a candidate's knowledge and skills in building, designing, and managing data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam covers a range of topics, including data processing, data storage, data warehousing, data modeling, and data architecture. Candidates are expected to have a deep understanding of these topics and be able to apply them in real-world scenarios.

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Databricks is a cloud-based data engineering platform that allows organizations to process large amounts of data quickly and efficiently. The platform leverages Apache Spark to perform data processing tasks and offers a wide range of tools and services to support data engineering workflows. Databricks also provides certification programs for data professionals who want to demonstrate their expertise in using the platform. One of these certifications is the Databricks Certified Professional Data Engineer exam.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. Unit
B. Integration
C. Manual
D. functional

Answer: A

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
Reference:
Software Testing Fundamentals: Unit Testing

NEW QUESTION # 92
Identify one of the below statements that can query a delta table in PySpark Dataframe API

A. Spark.read.table("table_name")
B. Spark.read.mode("delta").table("table_name")
C. Spark.read.format("delta").TableAs("table_name")
D. Spark.read.table.delta("table_name")
E. Spark.read.format("delta").LoadTableAs("table_name")

Answer: A

NEW QUESTION # 93
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
B. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
C. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
D. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
E. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

Answer: C

Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:
* Grant privileges on a database:
https://docs.databricks.com/en/security/auth-authz/table-acls/grant-privileges-database.html
* Privileges you can grant on Hive metastore objects:
https://docs.databricks.com/en/security/auth-authz/table-acls/privileges.html

NEW QUESTION # 94
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. 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.
C. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
D. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Answer: B

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.
Reference: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 95
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de- duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
B. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
C. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
D. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
E. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

Answer: C

Explanation:
The provided PySpark code performs the following operations:
* Reads Data from silver_customer_sales Table:
* The code starts by accessing the silver_customer_sales table using the spark.table method.
* Groups Data by customer_id:
* The .groupBy("customer_id") function groups the data based on the customer_id column.
* Aggregates Data:
* The .agg() function computes several aggregate metrics for each customer_id:
* F.min("sale_date").alias("first_transaction_date"): Determines the earliest sale date for the customer.
* F.max("sale_date").alias("last_transaction_date"): Determines the latest sale date for the customer.
* F.mean("sale_total").alias("average_sales"): Calculates the average sale amount for the customer.
* F.countDistinct("order_id").alias("total_orders"): Counts the number of unique orders placed by the customer.
* F.sum("sale_total").alias("lifetime_value"): Calculates the total sales amount (lifetime value) for the customer.
* Writes Data to gold_customer_lifetime_sales_summary Table:
* The .write.mode("overwrite").table("gold_customer_lifetime_sales_summary") command writes the aggregated data to the gold_customer_lifetime_sales_summary table.
* The mode("overwrite") specifies that the existing data in the
gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.
Conclusion:
When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: "The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job." References:
* PySpark DataFrame groupBy
* PySpark Basics

NEW QUESTION # 96
......

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2025 100% Free Databricks-Certified-Professional-Data-Engineer–Authoritative 100% Free Valid Exam Dumps | Valid Databricks Certified Professional Data Engineer Exam Dumps

Actual Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps – Pass Exam With Good Scores

Databricks Certified Professional Data Engineer Exam Sample Questions (Q91-Q96):

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