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Free PDF Certification Databricks-Certified-Professional-Data-Engineer Cost | Perfect Databricks-Certified-Professional-Data-Engineer Exam Collection: Databricks Certified Professional Data Engineer Exam

Free PDF Certification Databricks-Certified-Professional-Data-Engineer Cost | Perfect Databricks-Certified-Professional-Data-Engineer Exam Collection: Databricks Certified Professional Data Engineer Exam

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The Databricks Databricks-Certified-Professional-Data-Engineer exam is a comprehensive test that requires the candidates to demonstrate their ability to design and implement data processing systems on Databricks. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and performance-based tasks that assess the candidates' ability to solve real-world data engineering problems using Databricks. Databricks-Certified-Professional-Data-Engineer Exam is intended to be challenging, and candidates are expected to have a deep understanding of data engineering principles and best practices.

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By passing the DCPDE exam, data engineers can demonstrate their proficiency in using the Databricks platform to build scalable and reliable data pipelines. Databricks Certified Professional Data Engineer Exam certification can help data engineers advance their careers and increase their earning potential by showcasing their expertise in data engineering on Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
You are currently looking at a table that contains data from an e-commerce platform, each row contains a list of items(Item number) that were present in the cart, when the customer makes a change to the cart the entire information is saved as a separate list and appended to an existing list for the duration of the customer session, to identify all the items customer bought you have to make a unique list of items, you were asked to create a unique item's list that was added to the cart by the user, fill in the blanks of below query by choosing the appropriate higher-order function?
Note: See below sample data and expected output.
Schema: cartId INT, items Array<INT>

Fill in the blanks:
Fill in the blanks:
SELECT cartId, _(_(items)) FROM carts

  • A. ARRAY_DISTINCT, ARRAY_FLATTEN
  • B. ARRAY_DISTINCT, FLATTEN
  • C. ARRAY_DISTINCT, ARRAY_UNION
  • D. ARRAY_UNION, ARRAY_DISCINT
  • E. FLATTEN, ARRAY_DISTINCT

Answer: B

Explanation:
Explanation
FLATTEN -> Transforms an array of arrays into a single array.
ARRAY_DISTINCT -> The function returns an array of the same type as the input argument where all duplicate values have been removed.
Table Description automatically generated


NEW QUESTION # 13
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 named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. 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.

Answer: B

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 Reference: [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 # 14
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

  • A. Ensures that all steps interact correctly to achieve the desired end result
  • B. Improves the quality of your data
  • C. Troubleshooting is easier since all steps are isolated and tested individually
  • D. Yields faster deployment and execution times
  • E. Validates a complete use case of your application

Answer: B


NEW QUESTION # 15
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

  • A. Cluster Event Log
  • B. Executor's log file
  • C. Driver's log file
  • D. Workspace audit logs
  • E. Ganglia

Answer: E


NEW QUESTION # 16
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?

  • A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
  • B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
  • C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
  • D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
  • E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

Answer: D


NEW QUESTION # 17
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