DP-750 Sample Questions Answers

The correct answer is A. Photon is Azure Databricks' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso's requirement for 'fast and consistent performance for BI workloads' and 'production ingestion workloads that can scale automatically during telemetry spikes.'

Photon integrates transparently — no code changes are needed — and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.

Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what's needed — telemetry spikes require elastic scaling, not a locked node count.

The solution requires spark.readStream with format('cloudFiles') for Auto Loader, paired with .writeStream using mergeSchema=true and a checkpointLocation.

Auto Loader's cloudFiles source incrementally processes new JSON files without rescanning the entire directory. The mergeSchema option handles schema drift — when sensors add new fields, the target Delta table schema expands automatically instead of throwing a parse error. This directly addresses Contoso's requirement to 'support schema drift.'

The checkpointLocation is what gives the pipeline its resilience. Databricks writes the stream's committed offset and schema state to that path. If the cluster restarts, the engine reads the checkpoint and picks up exactly where it left off — no events are reprocessed, satisfying 'exactly-once semantics' and 'resume processing after failures without reprocessing the data.'

Without a checkpoint, the stream would restart from the beginning on every cluster bounce, which is precisely the problem Contoso is trying to eliminate.

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 answers are B and E.

The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:

Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel — in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.

Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting — the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.

Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.

The correct SQL statement uses the full three-part namespace finance.procurement.assets with the three specified columns.

In Unity Catalog, every object lives in a three-tier hierarchy: catalog → schema → table. Using the full path finance.procurement.assets guarantees the table lands in the right schema regardless of the session's current catalog or schema context. Omitting the catalog or schema name relies on the session default, which may not be finance.procurement — a silent mistake that's hard to catch.

The column names asset_id, asset_type, and asset_name must match the spec exactly. Unity Catalog applies access controls, lineage tracking, and tagging at the column level, so the names are meaningful beyond just the schema. Once created, any GRANT statements can target specific columns for fine-grained access control.

The correct answer is C — Enable schema evolution.

When new columns are added to the source data, a pipeline without schema evolution treats the unexpected columns as a schema mismatch and fails the write. Schema evolution, when enabled in an SDP pipeline, automatically adds those new columns to the target Delta table on the next pipeline run. The pipeline continues without intervention, and no historical data is lost.

Option A (disable schema enforcement) is the wrong lever — it removes all schema validation, which could allow corrupt or mistyped data into Table1. Schema evolution is a targeted, safer response.

Option B (row filters to exclude records with new columns) would silently discard valid records just because they carry extra fields — that's data loss. Option D (separate table per schema version) creates an explosion of tables as schemas evolve and makes downstream analytics significantly more complex. Schema evolution is the clean, built-in solution.