Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data is rejected if it doesn't match the clustering pattern.
B) New data is written to a staging area and clustered during scheduled maintenance.
C) New data remains unclustered until the next OPTIMIZE operation.
D) New data is automatically clustered during write operations.

2. 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)

3. The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
B) Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
C) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
D) Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
E) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

4. Review the following error traceback:

Which statement describes the error being raised?

A) There is no column in the table named heartrateheartrateheartrate
B) There is a type error because a DataFrame object cannot be multiplied.
C) The code executed was PvSoark but was executed in a Scala notebook.
D) There is a type error because a column object cannot be multiplied.
E) There is a syntax error because the heartrate column is not correctly identified as a column.

5. The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A) The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
B) Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
C) The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
D) The activity details table already exists; CHECK constraints can only be added during initial table creation.
E) The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

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