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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data transformation and modeling
|
| Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
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NEW QUESTION # 96
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
Answer: A
NEW QUESTION # 97
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?
Answer: B
Explanation:
To incorporate changes from your feature branch into the main branch while keeping every individual commit intact, you must use a fast-forward merge or a standard merge commit.
In the Azure Databricks Git folders UI or via standard Git operations, use the following command:
The Correct Command: git merge <branch-name>
Preserves History: Unlike a squash merge, a standard merge keeps all individual commit messages, authors, and timestamps.
Maintains Timeline: It seamlessly integrates the exact commit graph from your feature branch into the main branch.
Reference:
https://docs.databricks.com/aws/en/repos/git-operations-with-repos
NEW QUESTION # 98
What happens if incoming data violates Delta table schema?
Answer: B
Explanation:
Delta Lake enforces schema by default. If incoming data does not match schema, the write operation fails unless schema evolution is explicitly enabled. It does not auto-cast or append invalid data. Overwriting would require explicit command.
NEW QUESTION # 99
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
You need to create a new schema named schema2 that meets the following requirements:
- Is contained in catalog1
- Uses abfss://container@storageaccount.dfs.core.windows.net/data as
the managed location
Which SQL statement should you execute?
Answer: C
Explanation:
To create a new schema with a specific managed location in Azure Databricks Unity Catalog, use the CREATE SCHEMA command.
CREATE SCHEMA catalog_name.schema_name
MANAGED LOCATION 'abfss://container@storageaccount.dfs.core.windows.net/data'; Catalog Name: Replace catalog_name with your existing catalog.
Schema Name: Replace schema_name with your desired new schema name.Quotes: The storage path must be enclosed in single quotes.
Privileges: You must have CREATE SCHEMA privileges on the parent catalog and ownership (or adequate permissions) on the external location.
Reference:
https://dev.to/encorepartners/creating-your-first-catalog-schema-and-tables-in-databricks-20p3
NEW QUESTION # 100
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 # 101
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