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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 4: Prepare and process data | 30–35% | - Optimize and manage data storage
|
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NEW QUESTION # 27
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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.
NEW QUESTION # 28
Which tool is best for continuous ingestion of files landing in Azure Data Lake?
Answer: B
Explanation:
Auto Loader is optimized for incremental and continuous ingestion from cloud storage. It detects new files automatically and scales efficiently. Databricks Jobs schedule tasks but do not handle file detection. Logic Apps are workflow tools. ADF is batch-oriented.
NEW QUESTION # 29
You have an Azure Databricks workspace that contains a cluster named Cluster1.
Performance monitoring shows that Cluster1 is consistently overprovisioned for its batch workload:
* CPU utilization remains below 20 percent, including during peak processing periods.
* The workload is highly predictable and does not spike.
* The current node type already meets the workload requirements.
You need to reduce compute costs without increasing job duration.
What should you do?
Answer: B
Explanation:
Cluster1 is consistently underutilized, including during peak periods, which indicates that the workload does not require the currently allocated number of worker nodes. Because the workload is predictable and does not experience demand spikes, a smaller fixed-size cluster is more appropriate than maintaining an autoscaling range that can allocate unnecessary workers. Disabling autoscaling and reducing the fixed number of workers rightsizes the cluster and lowers compute costs while retaining the existing node type that already satisfies the workload requirements. Azure Databricks recommends using utilization metrics to determine the appropriate cluster size. Azure Databricks Photon might improve the execution speed of compatible workloads, but it does not directly correct excessive worker allocation. Decreasing the auto-termination timeout only reduces costs after the cluster becomes idle and does not reduce the resources consumed while the batch job is running. Selecting a larger node type would increase capacity and cost even though the existing nodes are already underutilized. Therefore, reducing the worker count is the most direct cost-optimization measure.
NEW QUESTION # 30
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
Answer: C
Explanation:
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.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/what-is-delta-live-tables
NEW QUESTION # 31
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Tabid.
Table! is written by batch jobs every hour and is queried frequently by filtering two columns named Customerld and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two features work together to keep performance consistent and update costs low:
OPTIMIZE with ZORDER BY (CustomerId, EventDate). Z-Ordering co-locates rows with the same CustomerId and EventDate values in the same Parquet files. When a query filters on those columns, the Delta engine uses file statistics to skip files that can ' t possibly contain matching rows (data skipping). As the table grows, skipping scales proportionally - query time stays consistent.
Deletion Vectors (delta.enableDeletionVectors = true). When a row is updated or deleted, instead of rewriting the entire Parquet file, Delta marks the affected row in a small companion deletion vector file. This dramatically reduces write amplification for the frequent compliance-driven updates and deletions the question describes. Actual file rewrites are deferred to the next OPTIMIZE run.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/data-skipping
Topic 1, Contoso Case StudyOverview
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
* 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.
* 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: |
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