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Databricks-Certified-Professional-Data-Engineer certification exam is a valuable credential for data engineers who work with Databricks. Databricks Certified Professional Data Engineer Exam certification demonstrates the candidate's expertise in Databricks technology and data engineering concepts. Databricks Certified Professional Data Engineer Exam certification also demonstrates the candidate's commitment to professional development and continuous learning.

Databricks is a leading cloud-based data platform that enables organizations to accelerate innovation and achieve their data-driven goals. To showcase their expertise in using the Databricks platform, data professionals can earn the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) certification. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data solutions on the Databricks platform.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. delayWrite("event_time", "10 minutes")
B. await("event_time + '10 minutes'")
C. withWatermark("event_time", "10 minutes")
D. slidingWindow("event_time", "10 minutes")
E. awaitArrival("event_time", "10 minutes")

Answer: C

Explanation:
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

NEW QUESTION # 58
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summary table?

A. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Answer: E

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 59
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Both commands will fail. No new variables, tables, or views will be created.
B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
C. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
D. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Answer: E

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 60
Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
B. Stream-static joins cannot use static Delta tables because of consistency issues.
C. The checkpoint directory will be used to track updates to the static Delta table.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
E. The checkpoint directory will be used to track state information for the unique keys present in the join.

Answer: A

Explanation:
Explanation
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified References:[Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 61
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

A. SHOW HISTORY table_name AS OF date_sub(current_date(), 1)
B. DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
C. TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
D. SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
E. SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'

Answer: D

Explanation:
Explanation
The answer is SELECT * FROM table_name TIMESTAMP as of date_sub(current_date(), 1) FYI, Time travel supports two ways one is using timestamp and the second way is using version number, Timestamp:
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000" Version Number:
1.SELECT count(*) FROM my_table VERSION AS OF 5238
2.SELECT count(*) FROM my_table@v5238
3.SELECT count(*) FROM delta.`/path/to/my/table@v5238`
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html

NEW QUESTION # 62
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