Databricks Certified Data Engineer Professional Exam: Databricks-Certified-Data-Engineer-Professional Exam Premium File as PDF & Test Engine

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

• A. All records are cached to an operational database and then the filter is applied
• B. The Hive metastore is scanned for min and max statistics for the latitude column
• C. The Delta log is scanned for min and max statistics for the latitude column
• D. The Parquet file footers are scanned for min and max statistics for the latitude column
• E. All records are cached to attached storage and then the filter is applied

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

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. Driver's log file
• C. Workspace audit logs
• D. Executor's log file
• E. Ganglia

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

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

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

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;

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

Which statement characterizes the general programming model used by Spark Structured Streaming?

• A. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
• B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
• C. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
• D. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
• E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).