PASS GUARANTEED 2025 DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER: HIGH HIT-RATE DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM DEMO TEST

Pass Guaranteed 2025 Databricks-Certified-Professional-Data-Engineer: High Hit-Rate Databricks Certified Professional Data Engineer Exam Demo Test

Pass Guaranteed 2025 Databricks-Certified-Professional-Data-Engineer: High Hit-Rate Databricks Certified Professional Data Engineer Exam Demo Test

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
A table is registered with the following code:
Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

  • A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
  • 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. 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.
  • D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
  • E. 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.

Answer: C


NEW QUESTION # 82
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

  • A. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
  • B. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
  • C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
  • D. No: the change data feed only tracks inserts and updates not deleted records.

Answer: C

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.


NEW QUESTION # 83
A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

  • A. They need to add the cloud_files(...) wrapper to the JSON file path
  • B. They need to add a COMMENT line at the beginning of the query
  • C. They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query
  • D. They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query
  • E. They need to add a live. prefix prior to json. in the FROM line

Answer: D


NEW QUESTION # 84
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

  • A.
  • B. preds.write.mode("append").saveAsTable("churn_preds")
  • C.
  • D.
  • E. preds.write.format("delta").save("/preds/churn_preds")

Answer: B


NEW QUESTION # 85
Which of the following functions can be used to convert JSON string to Struct data type?

  • A. FROM_JSON (json value, schema of json)
  • B. FROM_JSON (json value)
  • C. TO_STRUCT (json value)
  • D. CAST (json value as STRUCT)
  • E. CONVERT (json value, schema of json)

Answer: A

Explanation:
Explanation
Syntax
Copy
1.from_json(jsonStr, schema [, options])
Arguments
*jsonStr: A STRING expression specifying a row of CSV data.
*schema: A STRING literal or invocation of schema_of_json function (Databricks SQL).
*options: An optional MAP<STRING,STRING> literal specifying directives.
Refer documentation for more details,
https://docs.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/from_json


NEW QUESTION # 86
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