Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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. 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;
• B. 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;
• C. 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'));
• D. 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;

A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

• A. 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.
• B. 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.
• C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
• D. 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.

What statement is true regarding the retention of job run history?

• A. It is retained for 60 days, after which logs are archived
• B. It is retained for 90 days or until the run-id is re-used through custom run configuration
• C. It is retained until you export or delete job run logs
• D. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
• E. It is retained for 60 days, during which you can export notebook run results to HTML

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

• A. The badRecordsPath location is accumulating many small files.
• B. The source data is valid JSON but does not conform to the defined schema in some way.
• C. At some point, the upstream data provider switched everything to multi-line JSON.
• D. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".

Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

• A. ANALYZE
• B. BUCKETING
• C. OPTIMIZE
• D. COMPACT
• E. PARTITION BY