Microsoft DP-750 Exam Dumps

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.

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.

Task-level retries allow the ingestion task to recover automatically from transient failures without rerunning unrelated tasks or restarting the complete workflow. Downstream tasks remain governed by their dependencies and start only after ingestion succeeds. This provides focused failure recovery and reduces unnecessary compute consumption. Disabling retries and relying on manual execution directly contradicts the requirement for resilient, automated pipeline operation. Setting the retry count to zero also prevents automatic retry behavior. Restarting the workflow from the first task whenever any task fails would repeat completed processing, increase costs, and potentially reingest data unnecessarily. Lakeflow Jobs supports individual retry policies for tasks, including the number of retries and delay between attempts, making option D the most controlled and operationally efficient configuration. Microsoft Learn

The correct answer is A --- merge.

A Git merge combines the histories of two branches by creating a merge commit that joins them. Every individual commit from Branch1 remains visible in the repository log --- the full development history is preserved. This is the requirement: 'the solution must preserve the commit history in the repository.'

Option C (rebase) moves Branch1's commits on top of main by replaying them as new commits with new hashes. The end result looks like a linear history, but the original commit hashes are rewritten --- the prior history is not preserved in its original form. For a shared repository, rebase rewrites public history, which is considered problematic.

Option B (pull) fetches remote changes and merges or rebases them into the current branch --- it's used to sync with a remote, not to incorporate a feature branch. Option D (push) sends local commits to the remote but doesn't incorporate any branch into another.

Lakeflow Jobs provides native orchestration for multi-task Databricks workflows. Separate ingestion, cleansing, and curation tasks can be connected through explicit dependencies, ensuring that each stage starts only after its required upstream work succeeds. Each task can also have independent retry, notification, timeout, and compute settings, directly addressing the pipeline's resiliency requirements. Azure Data Factory could orchestrate notebooks, but it introduces another service when Lakeflow Jobs already provides the required functionality. A single notebook makes failures harder to isolate and can force successful stages to be rerun. Independently scheduled jobs rely on timing assumptions rather than actual task completion and can fail when an upstream stage runs longer than expected. Explicit Lakeflow Jobs dependencies provide reliable execution order and centralized monitoring. Microsoft Learn