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

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 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.

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.

groupBy

pivot

The groupBy( " Region " ) operation establishes one aggregation group for each geographical region in the DataFrame. The subsequent pivot( " Product " ) operation converts every distinct Product value into a separate output column. Finally, agg(F.sum( " Sales " )) calculates the total sales for every product within each region. The resulting DataFrame contains one row per region and one column for each product, with the corresponding aggregated sales value. cogroup combines grouped datasets and is not used for creating a conventional pivot table. coalesce controls the number of DataFrame partitions or replaces null values, depending on its context. The sum function is already provided inside the aggregation expression and therefore is not required in either blank.

The correct answer is B — values appear in both tables more than once.

Reading the query from the inside out: each subquery identifies values that appear more than once within their own table. The first subquery returns Column1 values that are duplicated in Table1. The second returns Column2 values that are duplicated in Table2. INTERSECT then returns only the values that appear in both result sets — meaning values that are duplicated in Table1 AND also duplicated in Table2.

Option A describes a UNION result (values in either table), not INTERSECT. Option C only considers duplicates in Table1, ignoring the Table2 condition — that would be the first subquery in isolation. Option D describes values exclusive to Table2 (EXCEPT or MINUS), the opposite of INTERSECT ' s requirement that values appear in both sets.

INTERSECT always requires membership in both operands — that ' s its fundamental definition in standard SQL.

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 targets mapping defines environment-specific deployment configurations within one databricks.yml file. Development and production targets can apply different resource settings or exclusions while sharing the bundle’s common definitions. This allows the app to be deployed to both environments and limits the development deployment to the required job without maintaining duplicate configuration files. Separate YAML files would duplicate shared settings and increase maintenance effort. The resources mapping declares jobs, pipelines, apps, and other Databricks resources but does not independently provide environment-specific deployment behavior. Variables provide reusable values and substitutions; they are not the primary mechanism for defining deployment environments. Declarative Automation Bundle targets are explicitly intended to model configurations such as development, staging, and production in a single bundle. Microsoft Learn

A CHECK constraint enforces a Boolean condition whenever data is inserted or updated. The constraint can require payment_amount > = 0 AND payment_amount < = 10000, causing a transaction containing an invalid value to fail instead of allowing the record into Payments. This provides storage-level data-quality enforcement regardless of which pipeline, notebook, or SQL statement performs the write. Row-level security controls which existing records users can see; it does not reject invalid writes. SELECT statements filter results only when they are executed and therefore cannot protect the underlying table. Table update triggers are not the standard Delta Lake mechanism for this requirement. Azure Databricks classifies CHECK constraints as enforced constraints and rejects transactions when their conditions are violated. 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.