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

A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

• A.
  
• B.
  
• C.
  
• D.
  

A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

• A. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
• B. Only workspace administrators can grant "Owner" privileges to a group.
• C. A user can only transfer job ownership to a group if they are also a member of that group.
• D. Other than the default "admins" group, only individual users can be granted privileges on jobs.
• E. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

• A. delayWrite("event_time", "10 minutes")
• B. slidingWindow("event_time", "10 minutes")
• C. awaitArrival("event_time", "10 minutes")
• D. await("event_time + `10 minutes'")
• E. withWatermark("event_time", "10 minutes")

A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

• A. Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
  Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
• B. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
• C. Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
  Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
• D. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

• A. SHOW TBLPROPERTIES dev.pii test
• B. DESCRIBE DETAIL dev.pii test
• C. DESCRIBE HISTORY dev.pii test
• D. SHOW TABLES dev
• E. DESCRIBE EXTENDED dev.pii test