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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| Topic 2: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 4: Prepare and process data | 30–35% | - Ingest and transform data
|
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NEW QUESTION # 24
Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 contains three tasks named Task1, Task2, and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 25
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 Delta table named Table1 Table1 stores order data and contains two columns named Ordertd and Quantity that have the following requirements:
* Orderld must always be present.
* Quantity must be greater than 0.
You need to enforce the requirements during ingestion by using validation checks. The solution must evaluate each record independently and minimize custom code.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Lakeflow pipeline expectations provide declarative row-level data-quality validation. An expectation uses a SQL Boolean expression that is evaluated independently for every record passing through the pipeline. This minimizes custom code because the required validation can be expressed directly as a constraint rather than implemented through a separate validation process or custom function. Microsoft Learn The correct expression is OrderId IS NOT NULL AND Quantity > 0. The first condition enforces the requirement that every order record contains an OrderId. The second condition ensures that Quantity is strictly greater than zero. OrderId > 0 would impose an additional numeric-range requirement that the question does not specify. Checking only Quantity > 0 would fail to validate the required presence of OrderId. Checking Quantity IS NOT NULL AND OrderId > 0 reverses the stated requirements.
Aggregate checks operate across collections of records rather than validating each record independently.
Schema inference determines data structure and types but does not enforce these business-value requirements.
Therefore, a row-level validation check containing both required conditions is the correct solution.
NEW QUESTION # 26
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
Answer: A
Explanation:
The correct answer is C - a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
SDP is tailor-made for exactly this pattern: ingest from cloud storage, transform through cleansing and enrichment stages, and publish Delta tables to Unity Catalog. What sets it apart from the other options is built- in monitoring (the pipeline graph shows row counts, expectation metrics, and run history) and automatic retries (failed tasks retry automatically based on pipeline settings, without manual re-run triggers).
Option A (Structured Streaming job) gives you the streaming engine but nothing else - monitoring, alerting, and retry logic all have to be built from scratch. Option B (scheduled notebook job) is a batch approach that requires manual monitoring and lacks the declarative lineage tracking SDP provides. Option D (Azure Data Factory with data flows) works but adds a separate Azure service to manage, introduces ADF licensing costs, and doesn ' t integrate natively with Unity Catalog governance.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/what-is-delta-live-tables
NEW QUESTION # 27
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.
Answer:
Explanation:
NEW QUESTION # 28
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes curated Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
Answer: C
Explanation:
The best choice is a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
Low Development Effort: Lakeflow SDP (formerly known as Delta Live Tables or DLT) is a completely declarative ETL framework. You simply define the target schemas and data transformations using standard SQL or Python. Databricks automatically manages the underlying operational complexities, state maintenance, task orchestration, and DAG dependencies for you.
Built-in Quality & Monitoring: It offers out-of-the-box data monitoring capabilities via Expectations, which allow you to specify data cleansing policies (like drop, retain, or fail on bad rows) with zero custom validation code. It also captures complete, automatic end-to-end data lineage and operational stats straight into Unity Catalog.
Built-in Resilience: Infrastructure failure handling and automatic retries are natively managed by the Lakeflow runtime.
Native Storage Ingestion: Using read_files() (Auto Loader) within SDP allows effortless, incremental ingestion of files from cloud object storage directly into curated Delta tables.
Reference:
https://docs.databricks.com/aws/en/ldp/
NEW QUESTION # 29
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