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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Topic 3: Prepare and process data | 30-35% | - Data ingestion
|
| Topic 4: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
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NEW QUESTION # 52
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?
Answer: D
Explanation:
Delta Sharing is designed to share governed data securely with recipients outside an Azure Databricks workspace. The external organization can query the shared data from its own compatible compute platform without receiving workspace access or requiring a Databricks SQL warehouse. Unity Catalog centrally controls which tables, views, or other objects are included in the share and which recipients can access them.
Moving files to an SFTP server creates additional copies and requires custom transfer and security administration. Lakeflow Connect is intended for ingesting data into Databricks rather than sharing curated data externally. Granting workspace access or creating a SQL warehouse would require the recipient to use Databricks-managed resources. Delta Sharing therefore provides the required open access model, centralized governance, and minimal administrative effort.
NEW QUESTION # 53
Which feature provides end-to-end tracking of data movement in Databricks?
Answer: C
Explanation:
Unity Catalog provides data lineage tracking across ingestion, transformation, and consumption layers. It helps visualize dependencies and compliance auditing. Spark logs are low-level and not structured for lineage. Query history is partial only.
NEW QUESTION # 54
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 # 55
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 # 56
Drag and Drop Question
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1.
You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location. The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 57
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