高效的DP-750考試證照及資格考試領先的供應商和免費PDF DP-750：Implementing Data Engineering Solutions Using Azure Databricks

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最新的 Microsoft Certified: Fabric Data Engineer Associate DP-750 免費考試真題 (Q20-Q25):

問題 #20
Hotspot Question
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：


問題 #21
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to run the following PySpark code.

答案：

解題說明：


問題 #22
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
Ensure that amount is always greater than 0.
Prevent an update to Table1 from being committed when data that violates rule1 is detected.
Which statement should you execute?

答案：B

解題說明：
The correct answer is C - @dlt.expect_or_fail.
Lakeflow Spark Declarative Pipelines (SDP) offers three expectation decorators, each with a different violation response:
@dlt.expect - logs the violation as a metric but writes all records, including bad ones, to the table. Suitable for monitoring only.
@dlt.expect_or_drop - drops violating records and continues the pipeline. The table receives only clean rows, but the pipeline update commits successfully.
@dlt.expect_or_fail - fails the entire pipeline update when a violation is detected. The table update is never committed. This is the correct choice when data integrity is non-negotiable: 'Prevent an update to Table1 from being committed when data that violates rule1 is detected.'
@dlt.expect_all_or_drop takes a dictionary of rules and drops violating rows but still commits - it doesn't halt the pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations


問題 #23
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.

答案：

解題說明：

Explanation:
Registering an ADLS Gen2 account as an external location in Unity Catalog requires three steps in a specific order:
Step 1: Create a Databricks Access Connector. This Azure resource wraps a system-assigned or user-assigned managed identity. It is the credential-less authentication bridge between Databricks and Azure Storage - no SAS tokens or access keys are stored anywhere in the workspace.
Step 2: Create a Storage Credential in Unity Catalog that references the Access Connector. This object tells Unity Catalog 'use this identity when accessing storage.' The Storage Credential is the reusable authentication object.
Step 3: Create an External Location that maps a Unity Catalog path to the specific ADLS Gen2 container using the Storage Credential. This is the object that grants Databricks users access to files under that path, and it respects the principle of least privilege by scoping access to a specific container.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/storage-credentials


問題 #24
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format('delta').option('readChangeFeed', 'true').table('db1.sales_orders') The readChangeFeed option switches the reader from the default 'new rows only' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed


問題 #25
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