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,
  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;
• B. 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'));
• 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;

A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

• A. Perform a full outer join on a unique key and overwrite existing data.
• B. Set the configuration delta.deduplicate = true.
• C. Rely on Delta Lake schema enforcement to prevent duplicate records.
• D. VACUUM the Delta table after each batch completes.
• E. Perform an insert-only merge with a matching condition on a unique key.

Which of the following is true of Delta Lake and the Lakehouse?

• A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
• B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
• C. Z-order can only be applied to numeric values stored in Delta Lake tables
• D. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
• E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

• A. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
• B. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
• C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
• D. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
• E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

• A. libraries
• B. workspace
• C. configure
• D. fs
• E. jobs