Online Access Free DP-750 Practice Test

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' ) The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
Ensure that amount is always greater than 0.
Prevent an update to Table1 from being committed when data that violates rule1 is detected.
Which statement should you execute?

• A.@dlt.expect_or_drop( " rule1 " , " amount > 0 " )
• B.@dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
• C.@dlt.expect( " rule1 " , " amount > 0 " )
• D.@dlt.expect_or_fail( " rule1 " , " amount > 0 " )

You have an Azure Databricks workspace that uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
* Salesperson
* Item
* 2019
* 2020
* 2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);

Correct Answer:

Explanation:
First dropdown: Year
Second dropdown: (2019, 2020, 2021)
The UNPIVOT operator converts the separate 2019, 2020, and 2021 columns into rows. Value becomes the output column containing the values previously stored in those year columns. Year becomes the output name column that identifies the original column from which each value came. Therefore, the expression must use Value FOR Year IN (2019, 2020, 2021). The Salesperson and Item columns are not included in the IN list because they remain identifier columns and are repeated for every resulting year row. A single source row consequently produces three output rows-one for each listed year. Selecting (Year) would reference an output name rather than the source columns that must be rotated.

Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
The right ingestion tool depends on the source characteristics:
File-based telemetry and maintenance data # Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.
Real-time telemetry from Event Hubs # Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying ' resume processing after failures without reprocessing. ' Structured maintenance data from PostgreSQL # JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.
Daily CSV ERP extracts (50-100 GB) # COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?

• A.file arrival
• B.scheduled
• C.manual
• D.continuous