Pass the Microsoft Microsoft Certified: Azure Databricks Data Engineer DP-750 Questions and answers with CertsForce

A time series profile repeatedly calculates data-quality metrics as a table changes and preserves those measurements over time. This makes it possible to observe whether the number of null values in each column is increasing, decreasing, or remaining stable as new records are added. A snapshot profile evaluates the table at a particular point in time and is therefore insufficient when the requirement is to analyze metric progression across multiple updates. Inference is associated with deriving information such as a schema or statistical characteristics; it is not the profile type used to maintain a historical sequence of monitoring measurements. Because the question requires repeated evaluation and the ability to observe changes throughout the dataset’s progression, the time series profile satisfies both requirements.

The correct answer is C. Column masks are the right tool when you need to partially expose sensitive data rather than hide it entirely. A column mask is a SQL function attached to a column that rewrites the returned value based on who is querying. For email_address, the mask returns only the substring after ' @ ' . For credit_card_number, it returns only the last four digits. Business analysts get useful data without seeing anything sensitive, and they can still query the table without errors.

Option A (row-level filters) controls which rows a user sees, not the values within a row — it can ' t partially redact a column. Option B (grant SELECT only on non-sensitive columns) removes the columns entirely, so analysts can ' t see even partial email or card values — that doesn ' t meet the ' can see only partial values ' requirement. Option D (column-level encryption) requires key management infrastructure and decryption at query time, which is significantly more complex than column masking for this use case.

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.

Registering an ADLS Gen2 account as an external location in Unity Catalog requires three steps in a specific order:

Step 1: Create a Databricks Access Connector. This Azure resource wraps a system-assigned or user-assigned managed identity. It is the credential-less authentication bridge between Databricks and Azure Storage — no SAS tokens or access keys are stored anywhere in the workspace.

Step 2: Create a Storage Credential in Unity Catalog that references the Access Connector. This object tells Unity Catalog ' use this identity when accessing storage. ' The Storage Credential is the reusable authentication object.

Step 3: Create an External Location that maps a Unity Catalog path to the specific ADLS Gen2 container using the Storage Credential. This is the object that grants Databricks users access to files under that path, and it respects the principle of least privilege by scoping access to a specific container.

The correct answers are B and D. Delta Lake ' s data retention behaviour is controlled by two distinct properties that work together:

delta.deletedFileRetentionDuration governs how long physically deleted data files are kept before VACUUM can remove them. Setting this to 30 days ensures deleted records remain on storage for the full audit window.

delta.logRetentionDuration controls how long the transaction log is retained. Time-travel queries (SELECT ... VERSION AS OF or TIMESTAMP AS OF) rely on the log to reconstruct historical table states — without 30 days of log, you can ' t query 30-day-old snapshots even if the data files are still there.

Both properties need to be set to 30 days to fully honour the requirement. Option A (delta.timeUntilArchived) doesn ' t exist in Delta Lake. Option C (autoCompact) manages file sizing, not retention. Option E (enableDeletionVectors) speeds up deletes but has no bearing on how long data is kept.

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.

The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.

SCD Type 1 overwrites the existing record whenever an attribute changes — no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.

SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time — that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.

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.

 CREATE OR REFRESH STREAMING TABLE

 city

CREATE OR REFRESH STREAMING TABLE defines a streaming table managed by Lakeflow Spark Declarative Pipelines. When the pipeline refreshes, Databricks incrementally processes newly available source data and maintains the resulting table. CREATE OR REPLACE TABLE would create a conventional table and does not provide the required streaming-table semantics. The query selects city AS city and calculates SUM(sales) AS total_sales. Because the aggregation must produce one result for each city, the GROUP BY expression must be city. Neither SUM(sales) nor total_sales belongs in the grouping clause: the former is the aggregate calculation, while the latter is only the alias assigned to its result. This produces continuously maintained city-level sales totals from the sales_order source table.

The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility — when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.

Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.

Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times — any future change must be applied in triplicate, which is a maintenance hazard. Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn ' t address cleansing or schema drift.