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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
It is similar to the DP-750 desktop-based software, with all the elements of the desktop practice exam. This mock exam can be accessed from any browser and does not require installation. The Implementing Data Engineering Solutions Using Azure Databricks (DP-750) questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based Implementing Data Engineering Solutions Using Azure Databricks (DP-750) practice test immediately through any operating system and browsers.
NEW QUESTION # 84
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.
Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 85
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?
Answer: D
Explanation:
The correct answer is A - INSERT INTO.
INSERT INTO appends all rows from the source to the target. Existing rows in table2 are untouched - the historical data stays intact - and the current year's rows from table1 are added. That is exactly what the requirement asks: 'preserve any existing data and minimise processing effort.' Option B (CREATE TABLE AS SELECT) would fail if table2 already exists, or if used with CREATE OR REPLACE it wipes table2 entirely before writing - historical data gone. Option C (INSERT OVERWRITE) replaces the entire table content with only the table1 rows, also destroying table2's existing data. Option D (CREATE OR REPLACE TABLE AS SELECT) explicitly drops and recreates the table, eliminating all historical records.
INSERT INTO is deliberately the simplest possible command here - one line, no special options, no risk of accidental data loss.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-insert-into
NEW QUESTION # 86
Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 87
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders.
Sales_orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows.
You need to load the daily data into Sales_orders. The solution must meet the following requirements:
- Preserve the existing data.
- Add only the new records.
- Minimize processing effort.
Which command should include in the loading strategy?
Answer: B
Explanation:
The best command for this scenario is INSERT INTO.
The INSERT INTO command appends new rows directly to the end of an existing Delta table.
Because your daily CSV file contains only new records and zero updates to existing rows, simply appending the data completely satisfies your requirements. It leaves all historical data untouched and requires the absolute lowest processing power because Databricks does not need to scan, modify, or rewrite any existing files.
Incorrect:
The UPDATE command is used to modify values in rows that already exist in the table based on a matching condition. It cannot be used to add entirely new rows to a table, and it requires a heavy scan of the data to find matches, which violates the requirement to keep processing low.
The INSERT OVERWRITE command deletes all the existing historical data in the table (or a specific partition) and replaces it with the data from the new CSV file. Using this would erase your historical sales data.
Reference:
https://medium.com/@jithujosekokken/understanding-data-patterns-in-medallion-architecture-full- incremental-and-change-only-loads-e33db28e51f4
NEW QUESTION # 88
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?
Answer: B
Explanation:
The correct answer is B - No.
This is a common Python-to-PySpark trap. In pure Python, comparing a value to None with != works as expected. In PySpark, null comparisons follow SQL null semantics: any comparison involving NULL returns NULL (not True or False). So df.filter(df.order_amount != None) doesn't evaluate to True for non-null rows
- the comparison itself returns NULL for null values, and Spark interprets NULL in a filter as False, effectively dropping null rows. But the behaviour is undefined in edge cases and is not the documented approach.
More practically, Python's None and Spark's SQL NULL are different concepts. PySpark Column objects don't support Python's native equality/inequality semantics for null checking. The result is typically an empty DataFrame or incorrect filtering behaviour.
Always use .isNotNull() or .isNull() for null checks in PySpark column expressions. These methods are specifically designed for SQL-null-aware comparisons and produce correct, predictable results.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 89
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