Microsoft Implementing Data Engineering Solutions Using Azure Databricks DP-750 Exam Dumps: Updated Questions & Answers (August 2026)

Concurrency setting: Limit concurrent runs to one.

Execution behavior: Queue the new run.

Limiting concurrent runs to one ensures that only one instance of Job1 can execute at a time. This prevents two hourly runs from simultaneously modifying the same tables, files, checkpoints, or downstream systems, thereby reducing the risk of duplicate processing and data corruption. When a scheduled trigger occurs while an earlier execution is still active, queueing the new run preserves that execution and starts it after the active run finishes. Allowing concurrent runs would violate the non-overlap requirement. Restarting the job during an overlap could interrupt partially completed work. Canceling the new run or skipping it would avoid simultaneous execution, but the scheduled processing interval could be lost. Single-run concurrency combined with queueing therefore serializes the executions without discarding scheduled work.

The correct answer is C — a Lakeflow Spark Declarative Pipelines (SDP) pipeline.

SDP is tailor-made for exactly this pattern: ingest from cloud storage, transform through cleansing and enrichment stages, and publish Delta tables to Unity Catalog. What sets it apart from the other options is built-in monitoring (the pipeline graph shows row counts, expectation metrics, and run history) and automatic retries (failed tasks retry automatically based on pipeline settings, without manual re-run triggers).

Option A (Structured Streaming job) gives you the streaming engine but nothing else — monitoring, alerting, and retry logic all have to be built from scratch. Option B (scheduled notebook job) is a batch approach that requires manual monitoring and lacks the declarative lineage tracking SDP provides. Option D (Azure Data Factory with data flows) works but adds a separate Azure service to manage, introduces ADF licensing costs, and doesn ' t integrate natively with Unity Catalog governance.

A foreign catalog mirrors the external ERP database and provides read-only access through Lakehouse Federation. Consequently, it cannot host managed volumes or locally created Delta tables, and its foreign tables cannot be altered to add analytics columns. The appropriate design is to create governed views in the managed analytics catalog and have those views reference the foreign tables through fully qualified catalog.schema.table names. This preserves the ERP data in its source system, avoids duplication, and exposes standardized curated objects through Unity Catalog. Permissions can then be granted on the managed views while access to underlying foreign objects remains controlled. Unity Catalog uses a three-level namespace, and foreign catalogs are specifically intended to make external database data queryable without copying it into Databricks-managed storage. Microsoft Learn

The correct answer is B — implement checkpointing.

A checkpoint is a durable record of the streaming job ' s progress written to ADLS Gen2 or DBFS after each successfully committed micro-batch. When the job restarts after a transient failure, it reads the checkpoint to find the last committed offset and resumes from that exact point — no data is reprocessed, no data is lost.

This satisfies all three requirements directly: checkpointing enables resumption from the failure point (not from the beginning), restart is fast because there ' s no replay overhead, and costs are minimised because no compute is wasted reprocessing records already handled.

Option A (decrease retry interval) makes the job retry sooner but doesn ' t control where it resumes from. Option C (alert and manual restart) adds human latency and doesn ' t prevent reprocessing without a checkpoint. Option D (increase minimum nodes) reduces the likelihood of resource-related failures but increases cost and doesn ' t address the recovery behaviour itself.

Installing a Maven package on every node requires changing Cluster1’s library configuration. The CAN MANAGE permission grants the cluster-level management capabilities required to add or remove libraries without giving User1 broader control over the entire workspace or Azure resource group. CAN RESTART allows a user to restart the cluster but does not provide sufficient authority to change its library configuration. Assigning the Azure Contributor role would grant excessive control over the resource group and does not represent the appropriate Databricks object-level permission. Making User1 a workspace administrator would provide substantially more privileges than necessary. Granting CAN MANAGE directly on Cluster1 therefore satisfies the operational requirement while limiting User1’s authority to the specific compute resource that must be modified.

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.

Production ingestion: Job compute

BI: Serverless SQL warehouse

Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all-purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.

The right ingestion tool depends on the source characteristics:

File-based telemetry and maintenance data → Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.

Real-time telemetry from Event Hubs → Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying ' resume processing after failures without reprocessing. '

Structured maintenance data from PostgreSQL → JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.

Daily CSV ERP extracts (50–100 GB) → COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.

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 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.