DP-750 Exam Dumps - Implementing Data Engineering Solutions Using Azure Databricks

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 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.

The correct answer is A — INSERT INTO.

INSERT INTO appends all rows from the source to the target. Existing rows in table2 are untouched — the historical data stays intact — and the current year ' s rows from table1 are added. That is exactly what the requirement asks: ' preserve any existing data and minimise processing effort. '

Option B (CREATE TABLE AS SELECT) would fail if table2 already exists, or if used with CREATE OR REPLACE it wipes table2 entirely before writing — historical data gone. Option C (INSERT OVERWRITE) replaces the entire table content with only the table1 rows, also destroying table2 ' s existing data. Option D (CREATE OR REPLACE TABLE AS SELECT) explicitly drops and recreates the table, eliminating all historical records.

INSERT INTO is deliberately the simplest possible command here — one line, no special options, no risk of accidental data loss.

For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:

DataFrames are immutable — every transformation returns a new DataFrame; the original is unchanged. Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.

Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns. Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.

Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.

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.