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| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
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NEW QUESTION # 70
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: A
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 # 71
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
Answer: A
Explanation:
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/
NEW QUESTION # 72
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
- The schemas and tables can be queried in Databricks.
- The schemas and tables appear alongside other Unity Catalog objects.
- The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You create a foreign catalog in Catalog Explorer.
You should create a Foreign Catalog using Lakehouse Federation.
Data Copying: Lakehouse Federation queries data directly in the source SQL Server without moving or copying it.
Seamless Integration: The database schemas and tables appear right inside Unity Catalog alongside your other data objects.Real-time Access: It provides immediate access to live SQL Server data.
Incorrect:
* You create a Databricks access connector.
* You create a Lakeflow Connect pipeline and connect it to DB1.
Data Copying: Lakeflow Connect is an ingestion tool that physically replicates and copies data into Databricks-managed storage (Delta tables).
Storage Costs: It violates your requirement to keep data out of Databricks storage.
* You create a new native catalog in Unity Catalog.
Note:
To expose the external SQL Server database in Unity Catalog without copying the data, you must use Lakehouse Federation.
Here are the step-by-step actions you need to take:
1. Create a Connection
Create a securable object in Unity Catalog that specifies the path and credentials to access the SQL Server database.
Go to Catalog Explorer or use SQL.
Select External Data > Connections.
Create a connection using the SQL Server connection details (URL, host, port, and database credentials).
*-> 2. Create a Foreign Catalog
Create a specific type of catalog in Unity Catalog that mirrors the external database.
Use the CREATE FOREIGN CATALOG SQL command or the Catalog Explorer UI.
Link this foreign catalog directly to the connection you created in step 1.
3. Query the DataOnce the foreign catalog is created, Unity Catalog automatically syncs the schemas and tables from SQL Server.
Reference:
https://docs.databricks.com/gcp/en/database-objects/
NEW QUESTION # 73
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You need to configure Cluster1 to meet the following requirements:
- The cluster must scale up automatically when workloads increase.
- The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
Enabling autoscaling and setting a 30-minute auto-termination timeout is the correct configuration to meet the goals.
Autoscaling handles fluctuating workloads, while auto-termination prevents you from paying for idle compute resources.
Dynamic Scaling: Autoscaling automatically adds workers during high loads and removes them when demand drops.
Cost Control: The 30-minute termination window ensures the cluster shuts down completely if no jobs are running, stopping all compute charges.
Reference:
https://community.databricks.com/t5/get-started-discussions/cluster-auto-termination-best- practices/td-p/75826
NEW QUESTION # 74
Which operation guarantees ACID compliance in Delta Lake?
Answer: A
Explanation:
Delta Lake ensures ACID compliance through its transaction log (Delta log). It tracks all changes, enabling consistency, isolation, and rollback capabilities. File append operations alone are not transactional. RDD transformations are low-level and not ACID-aware.
NEW QUESTION # 75
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