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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 4: Prepare and process data | 30–35% | - Ingest and transform data
|
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NEW QUESTION # 37
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
Explanation:
The symptoms indicate data skew. Spark partitions the stage's work into tasks, but a few partitions contain substantially more records than the others. Tasks processing the smaller partitions finish quickly, leaving the cluster underutilized while the stage waits for the oversized partitions. Caching would concern whether reused data is stored in memory and does not inherently explain an uneven distribution of input records. Shuffling transfers data between executors and can be expensive, but the decisive evidence is that only a few tasks process far more records. Spilling occurs when an executor cannot keep intermediate data in memory and writes it to disk; the scenario does not identify high spill volumes. Examining per-task input sizes in the Spark UI would confirm the skewed partition distribution.
NEW QUESTION # 38
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.isNotNull())
Does this meet the goal?
Answer: B
Explanation:
The correct answer is A - Yes.
df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark ' s filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.
This works correctly because isNotNull() is explicitly null-aware - unlike the != None comparison in Q52, it doesn ' t rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.
Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=[ ' order_amount ' ]) produce identical results. The choice between them is stylistic - isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 39
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks. Some tasks are business-critical, and others are not.
You need to configure alerting so that:
* Failures in non-critical tasks do not send notifications.
* Notifications are sent only for failures in the critical parts of the workflow.
What should trigger a notification?
Answer: D
Explanation:
The notification should be triggered by a task failure and configured only on the business-critical tasks.
Lakeflow Jobs supports notifications at both the overall job level and the individual task level. By attaching failure notifications specifically to the critical tasks, a notification is sent when one of those tasks fails, while failures in non-critical tasks do not generate unnecessary alerts. Microsoft Learn A job-level failure notification is too broad because it evaluates the result of the entire workflow. If a non- critical task causes Job1 to enter a failed state, the job-level notification could be sent even though the failed task was not considered business-critical. Job-success and task-success notifications report successful execution and do not satisfy the failure-alerting requirement.
Task-level failure notifications provide the required granularity and allow the alert configuration to follow the operational importance of each workflow component. If retries are configured, notifications can also be muted until the final retry to prevent repeated alerts for temporary failures. Therefore, task failure is the correct notification trigger.
NEW QUESTION # 40
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?
Answer: B
Explanation:
A Databricks notebook provides the flexibility required to implement complex, highly customized Python and PySpark transformations. Scheduling that notebook as a Lakeflow Jobs task supplies native daily orchestration, monitoring, retries, and compute management without introducing another service. Azure Data Factory data flows are oriented toward visually designed transformations and would add external orchestration complexity for logic already implemented most naturally in Python. A continuous job is inappropriate because the workload runs once per day rather than continuously. Spark Declarative Pipelines is effective for declarative batch and streaming ETL, but it is less direct when the core requirement emphasizes highly customized procedural Python transformations. A notebook task therefore provides the necessary programming freedom while keeping scheduling and operation inside Azure Databricks.
NEW QUESTION # 41
What happens if incoming data violates Delta table schema?
Answer: A
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 # 42
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