Choose Databricks-Certified-Data-Engineer-Professional most accurate study material

A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

• A. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
• B. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.
• C. Use Unity Catalog's row filters based on the region and column masks based on user roles.
• D. Create dynamic views for each user role and manage access with ACLs.

A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

• A. It runs VACUUM on the target table to purge conflicting records.
• B. It manually sorts incoming events by timestamp before applying changes.
• C. It ignores deletions if they arrive after updates for the same key.
• D. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.

What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

• A. PII masking is only required for direct identifiers.
• B. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
• C. Dynamic data masking is applied only at rest, so it does not affect query performance.
• D. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

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. 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;
• D. 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;

A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

• A.
  
• B.
  
• C.
  
• D.