Important Microsoft DP-750 Exam Questions

Answer: A

Explanation:

CORRECT ANSWE R: A - Enable Photon acceleration for a job compute cluster.

According to Microsoft Learn and the Azure Databricks documentation, Photon is a high-performance vectorized query engine written in C++ that accelerates Apache Spark workloads, especially ingestion and SQL operations. The Contoso technical requirement states: 'Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes' and 'Provide fast and consistent performance for BI workloads.' Photon on a job compute cluster directly addresses both speed and consistency for ingestion pipelines. Option B is incorrect because moving ingestion to shared compute would violate the requirement to isolate production from development. Option C is incorrect because increasing a fixed-node all-purpose cluster does not provide autoscaling. Option D is incorrect because disabling autoscaling would prevent the cluster from handling bursty telemetry workloads, directly contradicting the stated requirements.

Answer: C

Explanation:

CORRECT ANSWE R: C - A job cluster that uses autoscaling.

According to Microsoft Learn on Lakeflow Spark Declarative Pipelines compute configuration, job clusters (also called pipeline clusters) are the recommended compute type for SDP pipelines because they are created exclusively for the pipeline run and terminated when the pipeline stops, minimizing operational costs. Enabling autoscaling on the job cluster allows the pipeline to scale workers up during peak processing and scale down during idle periods. Option A (all-purpose cluster with autoscaling) is more expensive because all-purpose clusters are charged at a higher DBU rate and persist beyond pipeline runs. Option B (SQL warehouse) is designed for BI/SQL analytics, not SDP pipeline compute. Option D (single-node all-purpose cluster) does not autoscale and is not optimized for production pipeline workloads.

Answer: C

Explanation:

CORRECT ANSWE R: C - A Lakeflow Spark Declarative Pipelines (SDP) pipeline.

According to Microsoft Learn, Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) is purpose-built for exactly this scenario: ingesting files from cloud storage, applying cleansing and enrichment transformations, and writing Delta tables for analytics. SDP provides built-in monitoring (pipeline UI with expectation metrics and event logs), automatic retries on failure, data quality enforcement via expectations, and schema evolution --- all with minimal development effort using declarative Python or SQL. Option A (Spark Structured Streaming) requires manually implementing monitoring, retry logic, and alerting. Option B (scheduled notebook job) lacks built-in monitoring and retry capabilities. Option D (Azure Data Factory with data flows) requires additional Azure service setup, licensing, and does not integrate natively with Unity Catalog.

Answer: B

Explanation:

CORRECT ANSWE R: B - No.

According to Microsoft Learn and PySpark documentation, comparing a DataFrame column to Python's None using the != operator (df.order_amount != None) does not work correctly in PySpark. In PySpark, null comparisons using standard Python equality/inequality operators (==, !=) with None follow SQL null semantics --- any comparison with NULL returns NULL (not True or False). As a result, df.filter(df.order_amount != None) does not correctly filter out null rows; it may return unexpected results or fail silently. The correct PySpark approach is to use the isNotNull() method: df.filter(df.order_amount.isNotNull()), or df.dropna(subset=['order_amount']). Using Python's None with comparison operators is a common mistake when transitioning from Python to PySpark.

Answer: D

Explanation:

CORRECT ANSWE R: D - Add a NOT NULL constraint to transaction_id and a CHECK constraint to amount.

According to Microsoft Learn on Delta Lake table constraints, Delta tables support two types of data quality constraints: NOT NULL constraints and CHECK constraints. A NOT NULL constraint ensures that the specified column cannot contain null values --- applying this to transaction_id satisfies 'transaction_id must never be null.' A CHECK constraint enforces a boolean expression on a column --- applying CHECK (amount > 0) ensures that only positive amounts are written. When a row violates either constraint, Delta Lake rejects the write operation, satisfying 'invalid records must be rejected when data is written to the Sales table.' These are table-level constraints enforced by the Delta engine on every write. Option A (SELECT with WHERE) is a read-time filter, not a write-time constraint. Options B and C (RLS and views) are read-time access controls, not write constraints.