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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Topic 3: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 4: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
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NEW QUESTION # 78
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 foreign catalog in Catalog Explorer.
Does this meet the goal?
Answer: B
Explanation:
The correct answer is A - Yes.
A foreign catalog created through Lakehouse Federation in Catalog Explorer is the correct solution for all three requirements. Here's why it works:
The schemas and tables of DB1 can be queried in Databricks - Lakehouse Federation pushes the query down to the external SQL Server and returns results, so analysts write normal SQL in Databricks.
They appear alongside other Unity Catalog objects - the foreign catalog sits in the same three-tier hierarchy as native catalogs, schemas, and tables, visible in Catalog Explorer alongside all other Unity Catalog assets.
The data is NOT copied into Databricks-managed storage - foreign catalogs query data in place at the source; nothing is replicated or materialised in Databricks storage.
This is exactly the scenario Lakehouse Federation was built for.
Reference: https://learn.microsoft.com/en-us/azure/databricks/query-federation/lakehouse-federation
NEW QUESTION # 79
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
Answer: C
Explanation:
The databricks bundle init command initializes a new bundle project from a selected default or custom project template. It prompts for relevant project values and creates the local project structure and starter configuration files. It does not deploy resources; deployment is performed with databricks bundle deploy. It does not merely validate an existing configuration, because validation uses databricks bundle validate. Running deployed bundle resources requires databricks bundle run followed by the resource key. The distinction matters in an automated bundle lifecycle: initialization scaffolds the project, validation checks configuration, deployment creates or updates workspace resources, and run starts a configured job or pipeline. Therefore, option A accurately describes the initialization command's function. Microsoft Learn
NEW QUESTION # 80
You have an Azure Databricks workspace that contains a Delta table named Table1.
Table1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
To diminish storage costs while preserving 60 days of time travel history, you must perform the following two actions: Set the delta.deletedFileRetentionDuration table property to 60 days and Run the vacuum command on the table.
Set the delta.deletedFileRetentionDuration table property to 30 days
This property controls how long data files must be deleted before they become eligible for removal by a cleanup process. By default, it is set to 7 days. Increasing it to 60 days ensures that Delta Lake preserves the underlying parquet files required to query any historical snapshot within your 30-day time travel window.
Run the vacuum command on the tableChanging the retention property alone does not delete files or reduce costs. You must explicitly execute the VACUUM command. The command scans the table and permanently deletes uncommitted or deleted data files that are older than the 60- day threshold defined by your retention duration, thereby freeing up storage space.
Incorrect:
[Not C]
Set the delta.logRetentionDuration table property to 30 days
This property controls how long the transaction log (_delta_log) history is kept, which defaults to
30 days. While the transaction log is required for time travel, modifying this property alone does not delete the heavy data files causing high storage costs. Furthermore, it governs the logs rather than the actual deleted data files.
Reference:
https://www.cloudmatter.io/post/data-audit-with-databricks-delta-time-travel
NEW QUESTION # 81
You have an Azure Databricks account that contains workspaces enabled for Unity Catalog.
You need to implement audit logging to meet the following requirements:
* Capture audit logs for all the workspaces in the account.
* Retain the audit logs for 90 days.
* Minimize storage and ingestion costs.
The logs will be reviewed only during security investigations and will NOT be queried regularly.
To where should you send the audit logs?
Answer: C
Explanation:
An Azure Storage account provides durable, comparatively low-cost retention for diagnostic and audit logs that are accessed infrequently. A lifecycle or retention policy can preserve the logs for 90 days and then remove them automatically. This matches an investigation-only access pattern without paying the ingestion and indexing charges associated with Log Analytics. Azure Monitor metrics stores numerical monitoring measurements, not the complete audit-event records required here. Azure Event Hubs is a streaming transport intended to forward events to consumers and is not the final long-term retention destination. Log Analytics is appropriate when teams need frequent querying, dashboards, and alerting, but those capabilities introduce unnecessary cost for logs reviewed only during occasional investigations. Storage therefore best satisfies centralized retention and cost requirements.
NEW QUESTION # 82
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 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.dropna(subset=["order_amount"])
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You run the following expression.
df.dropna(subset=["order_amount"])
The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null.
* You run the following expression.
df.filter(df.order_amount.isNotNull())
To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions:
Option 1: Python/PySpark API (Recommended)
pythondf_clean = df.filter(df["order_amount"].isNotNull())
Incorrect:
* You run the following expression.
df.fillna(0, subset=['order_amount'])
* You run the following expression.
df.filter(df.order_amount != None)
Reference:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna
NEW QUESTION # 83
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