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Microsoft - DP-750 Pass Guaranteed 1 of 17 Only Solution2Pass for Any Exam Category Breakdown Category Number of Questions Prepare and process data 4 Deploy and maintain data pipelines and workloads 4 Set up and configure an Azure Databricks environment 2 TOTAL 10 Exam Topic Breakdown Exam Topic Number of Questions Topic 2 : Misc. Questions 5 Topic 1 : Contoso Case Study 5 TOTAL 10 Microsoft - DP-750 Pass Guaranteed 2 of 17 Only Solution2Pass for Any Exam A. B. Topic 2, Misc. Questions Question #:1 - - [Prepare and process data] (Exam Topic 2) You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders. You load the Orders table into an Apache Spark DataFrame named df. You need to create a DataFrame that excludes rows where the order amount is null. Solution: You run the following expression. df.filter(df.order_amount.isNotNull()) Does this meet the goal? Yes No Answer: A Explanation The correct answer is A — Yes. df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark's filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed. This works correctly because isNotNull() is explicitly null-aware — unlike the != None comparison in Q52, it doesn't rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark. Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=['order_amount']) produce identical results. The choice between them is stylistic — isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns. Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics Question #:2 - - [Deploy and maintain data pipelines and workloads] (Exam Topic 2) You have an Azure Databricks workspace Microsoft - DP-750 Pass Guaranteed 3 of 17 Only Solution2Pass for Any Exam A. B. C. D. A. You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use? an all-purpose cluster that uses autoscaling the existing SQL warehouse a job cluster that uses autoscaling a single-node, all-purpose cluster Answer: C Explanation The correct answer is C — a job cluster that uses autoscaling. Job clusters (also called pipeline clusters in the SDP context) are created exclusively for a pipeline run and terminated when the pipeline stops. You pay only for what the pipeline uses, and there's no idle cost between runs. Autoscaling on a job cluster lets the pipeline expand during heavy processing and contract during lighter stages — the combination of on-demand lifecycle and elastic scaling gives the lowest operational cost. Option A (all-purpose cluster) runs at a higher DBU rate and persists beyond the pipeline's lifecycle, meaning you're paying for it even when the pipeline isn't running. Option B (SQL warehouse) is optimised for interactive BI and ad-hoc queries, not for the batch/streaming workloads SDP pipelines run. Option D (single- node all-purpose) has no scaling, runs at the all-purpose DBU rate, and is capped at one node — unsuitable for any production pipeline. Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/configure-pipeline Question #:3 - - [Deploy and maintain data pipelines and workloads] (Exam Topic 2) You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1. You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements: • Invalid records must NOT be written to Table 1. • Invalid records must be preserved for review. • Minimize development effort What should you do? Implement advanced logic to quarantine the invalid records. Microsoft - DP-750 Pass Guaranteed 4 of 17 Only Solution2Pass for Any Exam B. C. D. A. B. C. D. Define a pipeline expectation. Run were clauses in downstream queries to filter out invalid records. Add a check constraint to Table1 Answer: B Explanation The correct answer is B — define a pipeline expectation. SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline's event log as expectation violations — available for review without any extra code. Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code. Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views — they're not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the 'pipeline continues to run' requirement. Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations Question #:4 - - [Deploy and maintain data pipelines and workloads] (Exam Topic 2) You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1. From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main. When you attempt to merge Branch! into main, the merge fails due to conflicts. You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches. What should you do? From Workspace1, clone Branch! as a new repository. Apply the changes directly to the main branch. From Workspace1, clone the mam branch as a new repository. Microsoft - DP-750 Pass Guaranteed 5 of 17 Only Solution2Pass for Any Exam D. A. B. C. D. E. Apply the main branch changes to Branch! and resolve the conflicts. Answer: D Explanation The correct answer is D — apply the main branch changes to Branch1 and resolve the conflicts. When a merge fails due to conflicts, the right workflow is to bring main's changes into the feature branch, resolve conflicts there, and then merge the clean feature branch into main. This is the standard Git conflict resolution pattern — resolve in the feature branch, not in main — because it protects the main branch from partial or broken states during resolution. Option A (clone Branch1 as a new repository) creates a disconnected copy; it doesn't resolve the conflict and breaks the relationship with the remote. Option B (apply changes directly to main) bypasses the feature branch entirely and risks overwriting the other developer's work. Option C (clone main as a new repository) again creates a disconnected copy — none of Branch1's changes would be incorporated, and history would be lost. Reference: https://learn.microsoft.com/en-us/azure/databricks/repos/git-operations-with-repos Question #:5 - - [Set up and configure an Azure Databricks environment] (Exam Topic 2) You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements; • The cluster must scale up automatically when workloads increase. • The cluster must scale down automatically when workloads decrease. The solution must minimize costs. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Disable Photon acceleration. Apply a compute policy that enables users to manage the cluster settings. Configure Cluster1 to terminate after 30 minutes of inactivity. Enable autoscaling for Cluster1. Specify a fixed number of workers. Answer: C D Explanation Microsoft - DP-750 Pass Guaranteed 6 of 17 Only Solution2Pass for Any Exam The correct answers are C and D. Together they deliver cost-efficient autoscaling: D (Enable autoscaling) allows the cluster to grow when workloads increase and shrink when they ease off. This satisfies both scale-up and scale-down requirements without manual intervention. C (Auto-termination after 30 minutes of inactivity) ensures the cluster stops entirely when no work is running, eliminating the cost of an idle cluster. This is the cheapest possible state. Option A (disable Photon) reduces compute acceleration — that's a performance regression with no meaningful cost benefit for autoscaling. Option B (compute policy that lets users manage settings) adds governance overhead and doesn't address scaling behaviour. Option E (fixed number of workers) is the opposite of autoscaling — a static worker count that either over-provisions during quiet periods or under- provisions during peaks. Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/configure#autoscaling Microsoft - DP-750 Pass Guaranteed 7 of 17 Only Solution2Pass for Any Exam Topic 1, Contoso Case Study Overview Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region. Workspace1 is enabled for Unity Catalog. Workspace1 contains all-purpose clusters for both development and production workloads. The company's Azure environment contains: • In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub • A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data • An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data Company information Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America. Data Environment Contoso ingests the following operational and business data: • Telemetry data: More than 40,000 loT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift. • Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts. • Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server. • External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts. • ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information. Problem Statements The company's existing analytics environment has several issues: Ingestion Microsoft - DP-750 Pass Guaranteed 8 of 17 Only Solution2Pass for Any Exam • Telemetry pipelines fall behind during peak loads. • Telemetry ingestion fails when schema drift occurs. • Streaming pipelines reprocess events after a pipeline restarts. Compute • Production and development workloads run on the same all-purpose clusters. • Production and development workloads do NOT support autoscaling or workload isolation. Governance • The ERP data is duplicated across systems and development teams. • Naming conventions are inconsistent across development teams, regions, and products. • Ownership of the loT sensors changes over time, and analysts must track the full history of the ownership. • Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names. Historical values are NOT required. Pipeline operations • Pipelines lack resiliency, alerting, and centralized scheduling. Planned Changes Contoso plans to implement the following changes: • Implement scalable data pipeline orchestration. • Create a managed analytics catalog in Unity Catalog. • Implement a consistent approach to creating curated datasets. • Establish a centralized governance model across ingestion, cleansed, and curated layers. • Grant data engineers access to the ERP tables by using minimal development effort. • Adopt a compute strategy that isolates production workloads and supports autoscaling. • Adopt a slowly changing dimension (SCD) approach to address current data modeling issues. Technical Requirements Contoso identifies the following environment and compute requirements: • Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes. Microsoft - DP-750 Pass Guaranteed 9 of 17 Only Solution2Pass for Any Exam • Provide fast and consistent performance for business intelligence (Bl) workloads. • Prevent development activity from affecting production pipelines. • Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters. Contoso identifies the following data ingestion and processing requirements: • Auto-scale ingestion pipelines to handle bursty workloads. • Handle schema drift for the maintenance and telemetry data. • Ingest file-based telemetry data by using minimal operational effort. • Store all the ingested data in a format that supports incremental processing. • Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics. • Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server. • Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog. • Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data. Contoso identifies the following data modeling and optimization requirements: • Build curated tables that standardize business logic. • Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required. Contoso identifies the following pipeline deployment and operation requirements: |^ • Orchestrate multi-step ingestion and transformation workflows. • Define a clear execution order and dependencies. • Automatically retry failed steps and notify operators. • Schedule ingestion and transformation workloads consistently. Governance Requirements Contoso identifies the following governance requirements: • Centralize the metadata catalog. Microsoft - DP-750 Pass Guaranteed 10 of 17 Only Solution2Pass for Any Exam A. B. C. D. • Provide isolated development areas that follow standard naming conventions. • Establish a consistent structure for organizing raw, cleansed, and curated data. • Provide a read-only mechanism to reference the ERP data through a foreign catalog. Business Requirements Contoso identifies the following business requirements: • Improve ingestion reliability and reduce operational effort. • Standardize data definitions across development teams. Question #:6 - - [Deploy and maintain data pipelines and workloads] (Exam Topic 1) You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data. Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements. What should you do? Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script. Create three tasks that each contains the identical logic and use task retries. Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands. Create separate tasks for ingestion, cleansing, and curation. Answer: D Explanation 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. Microsoft - DP-750 Pass Guaranteed 11 of 17 Only Solution2Pass for Any Exam Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/ Question #:7 - - [Prepare and process data] (Exam Topic 1) Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Answer: Explanation Microsoft - DP-750 Pass Guaranteed 12 of 17 Only Solution2Pass for Any Exam 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. Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/ Question #:8 - - [Prepare and process data] (Exam Topic 1) You need to complete the PySpark code for the Spark Structured Streaming pipelines. The solution must meet the data ingestion and processing requirements. How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Microsoft - DP-750 Pass Guaranteed 13 of 17 Only Solution2Pass for Any Exam Answer: Microsoft - DP-750 Pass Guaranteed 14 of 17 Only Solution2Pass for Any Exam Explanation 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.' Microsoft - DP-750 Pass Guaranteed 15 of 17 Only Solution2Pass for Any Exam A. B. C. D. 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. Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/schema C:\Users\Waqas Shahid\Desktop\Mudassir\Untitled.jpg Question #:9 - - [Set up and configure an Azure Databricks environment] (Exam Topic 1) You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements. What should you do? Enable Photon acceleration for a job compute cluster. Move the ingestion pipelines to shared compute. Increase an all-purpose cluster to a larger fixed node type. Disable autoscaling for a job compute cluster. Answer: A Explanation 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. Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/photon Microsoft - DP-750 Pass Guaranteed 16 of 17 Only Solution2Pass for Any Exam Question #:10 - - [Prepare and process data] (Exam Topic 1) Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Answer: Explanation 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. Microsoft - DP-750 Pass Guaranteed 17 of 17 Only Solution2Pass for Any Exam Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge About solution2pass.com solution2pass.com was founded in 2007. We provide latest & high quality IT / Business Certification Training Exam Questions, Study Guides, Practice Tests. We help you pass any IT / Business Certification Exams with 100% Pass Guaranteed or Full Refund. 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