Online DP-750 Questions Video

The correct answer is C — the Databricks CLI.

Databricks Asset Bundles are deployed using the Databricks CLI (v0.205+) with the commands databricks bundle validate, databricks bundle deploy, and databricks bundle run. The CLI reads the databricks.yml file, resolves the target environment settings, and creates or updates all declared resources (jobs, pipelines, apps) in the workspace. This is the documented, supported deployment mechanism for DABs and integrates naturally into CI/CD pipelines.

Option A (Azure Developer CLI / azd) is a general Azure IaC tool with no native understanding of Databricks bundles. Option B (Git folders) syncs notebook and file content from a Git repository into a workspace — useful for code, but it doesn't deploy Lakeflow Jobs, SDP pipelines, or bundle configurations. Option D (Azure CLI) manages Azure infrastructure (resource groups, storage accounts, etc.) and has no native DAB deployment support.

Two separate cluster settings address the two requirements:

Autoscaling handles the first requirement — dynamically adding workers when the workload is heavy and removing them when it lightens. You set a minimum and maximum node count, and Databricks adjusts the cluster size between those bounds based on task queue depth.

Auto-termination handles the second — the cluster shuts itself down after a configurable idle period (e.g., 30 minutes with no active queries), preventing wasted spend on a development cluster left running overnight.

These two settings are independent and complementary: autoscaling manages horizontal elasticity during active use, while auto-termination manages complete shutdown during inactivity. Both are configured in the cluster creation UI under the Compute section.

The correct answer is A — Yes.

df.dropna(subset=['order_amount']) is the idiomatic PySpark way to remove rows where a specific column contains a null. It inspects only the columns listed in subset and drops any row where those columns are null. The resulting DataFrame contains only rows where order_amount is not null — exactly what the requirement asks for.

The subset parameter is important: without it, dropna() would drop rows where ANY column is null, which could incorrectly exclude rows that have nulls in other columns but a valid order_amount. By specifying subset=['order_amount'], the filter is applied precisely and only to the column in question.

This method is semantically equivalent to df.filter(df.order_amount.isNotNull()) and to the SQL clause WHERE order_amount IS NOT NULL. Both are correct — dropna with a subset is arguably the more readable Pythonic approach.

The correct answer is B — No.

This is a common Python-to-PySpark trap. In pure Python, comparing a value to None with != works as expected. In PySpark, null comparisons follow SQL null semantics: any comparison involving NULL returns NULL (not True or False). So df.filter(df.order_amount != None) doesn't evaluate to True for non-null rows — the comparison itself returns NULL for null values, and Spark interprets NULL in a filter as False, effectively dropping null rows. But the behaviour is undefined in edge cases and is not the documented approach.

More practically, Python's None and Spark's SQL NULL are different concepts. PySpark Column objects don't support Python's native equality/inequality semantics for null checking. The result is typically an empty DataFrame or incorrect filtering behaviour.

Always use .isNotNull() or .isNull() for null checks in PySpark column expressions. These methods are specifically designed for SQL-null-aware comparisons and produce correct, predictable results.