2025 FREE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VCE DUMPS | ACCURATE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER 100% FREE TEST ENGINE

2025 Free Databricks-Certified-Professional-Data-Engineer Vce Dumps | Accurate Databricks-Certified-Professional-Data-Engineer 100% Free Test Engine

2025 Free Databricks-Certified-Professional-Data-Engineer Vce Dumps | Accurate Databricks-Certified-Professional-Data-Engineer 100% Free Test Engine

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

  • A. No permissions
  • B. Can Run
  • C. Can Edit
  • D. Can Read
  • E. Can Manage

Answer: D

Explanation:
Explanation
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to makeany changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.


NEW QUESTION # 30
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

  • A. Network I/O never spikes
  • B. Bytes Received never exceeds 80 million bytes per second
  • C. Total Disk Space remains constant
  • D. CPU Utilization is around 75%
  • E. The five Minute Load Average remains consistent/flat

Answer: D


NEW QUESTION # 31
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:
Explanation
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.


NEW QUESTION # 32
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. 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.
  • B. 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.
  • 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 Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  • E. 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.

Answer: A

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 # 33
Which statement describes Delta Lake optimized writes?

  • A. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
  • B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
  • C. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
  • D. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.

Answer: D

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.
References:
* Databricks documentation on Delta Lake performance tuning:
https://docs.databricks.com/delta/optimizations/auto-optimize.html


NEW QUESTION # 34
......

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