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Databricks Certified Data Engineer Professional Sample Questions:

1. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A) import sys
date = sys.argv[1]
B) input_dict = input()
date= input_dict["date"]
C) date = dbutils.notebooks.getParam("date")
D) dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
E) date = spark.conf.get("date")

2. 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) fs
B) jobs
C) libraries
D) configure
E) workspace

3. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
B) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
C) The update throws an error because changes to existing columns in the target schema are not supported.
D) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

4. A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A) Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
B) Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
C) Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
D) Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.

5. 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) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
B) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
C) No; the change data feed only tracks inserts and updates not deleted records.
D) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
E) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

Solutions: