Databricks Databricks-Certified-Data-Engineer-Professional valid study dumps : Databricks Certified Data Engineer Professional Exam

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

• A. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
• B. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
• C. Instruct the marketing team to download results as a CSV and email them to the sales organization.
• D. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
• E. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:

Proposed query:

Which step must also be completed to put the proposed query into production?

• A. Specify a new checkpointlocation
• B. Register the data in the "/item_agg" directory to the Hive metastore
• C. Increase the shuffle partitions to account for additional aggregates
• D. Remove .option (mergeSchema', true') from the streaming write
• E. Run REFRESH TABLE delta, /item_agg'

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

• A. Manual
• B. End-to-end
• C. Integration
• D. functional
• E. Unit

Which statement describes a key benefit of an end-to-end test?

• A. It pinpoint errors in the building blocks of your application.
• B. It closely simulates real world usage of your application.
• C. It provides testing coverage for all code paths and branches.
• D. It makes it easier to automate your test suite

A facilities-monitoring team is building a near-real-time PowerBI dashboard off the Delta table device_readings:
Columns:
device_id (STRING, unique sensor ID)
event_ts (TIMESTAMP, ingestion timestamp UTC)
temperature_c (DOUBLE, temperature in °C)
Requirement:
For each sensor, generate one row per non-overlapping 5-minute
interval, offset by 2 minutes (e.g., 00:02-00:07, 00:07-00:12, ...).
Each row must include interval start, interval end, and average
temperature in that slice.
Downstream BI tools (e.g., Power BI) must use the interval timestamps
to plot time-series bars.

• A. SELECT device_id,
  event_ts,
  AVG(temperature_c) OVER (
  PARTITION BY device_id
  ORDER BY event_ts
  RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
  ) AS avg_temp_5m
  FROM device_readings
  WINDOW w AS (window(event_ts, '5 minutes', '2 minutes'));
• B. SELECT device_id,
  date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
• C. WITH buckets AS (
  SELECT device_id,
  window(event_ts, '5 minutes', '2 minutes', '5 minutes') AS win,
  temperature_c
  FROM device_readings
  )
  SELECT device_id,
  win.start AS bucket_start,
  win.end AS bucket_end,
  AVG(temperature_c) AS avg_temp_5m
  FROM buckets
  GROUP BY device_id, win
  ORDER BY device_id, bucket_start;
• D. SELECT device_id,
  window.start AS bucket_start,
  window.end AS bucket_end,
  AVG(temperature_c) AS avg_temp_5m
  FROM device_readings
  GROUP BY device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes') ORDER BY device_id, bucket_start;