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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Topic 2: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Topic 4: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
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NEW QUESTION # 51
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The solution is a Key Vault-backed secret scope combined with dbutils.secrets.get() in the notebook.
An Azure Key Vault-backed secret scope does not copy the secret value into Databricks. Every call to dbutils.
secrets.get() fetches the live current value directly from Key Vault at runtime. This means when the cloud security team rotates storageKey in Key Vault, the notebook automatically picks up the new value on its next run - no Databricks configuration changes needed. That satisfies 'always retrieve the latest value.' The principle of least privilege is upheld because only the secret scope has read access to Key Vault through a service principal or managed identity, and individual users never see the raw secret value - Databricks redacts it in logs automatically.
A Databricks-backed scope stores a copy of the secret inside Databricks, so it wouldn't automatically reflect Key Vault updates. Hardcoding or using environment variables would expose the secret in plain text.
Reference: https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes
NEW QUESTION # 52
You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table! is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table!
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?
Answer: B
Explanation:
The correct answer is C - Enable schema evolution.
When new columns are added to the source data, a pipeline without schema evolution treats the unexpected columns as a schema mismatch and fails the write. Schema evolution, when enabled in an SDP pipeline, automatically adds those new columns to the target Delta table on the next pipeline run. The pipeline continues without intervention, and no historical data is lost.
Option A (disable schema enforcement) is the wrong lever - it removes all schema validation, which could allow corrupt or mistyped data into Table1. Schema evolution is a targeted, safer response.
Option B (row filters to exclude records with new columns) would silently discard valid records just because they carry extra fields - that's data loss. Option D (separate table per schema version) creates an explosion of tables as schemas evolve and makes downstream analytics significantly more complex. Schema evolution is the clean, built-in solution.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/schema-evolution
NEW QUESTION # 53
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
Answer: D
Explanation:
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don ' t prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely. Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints
NEW QUESTION # 54
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
spark.readStream.format('eventhubs').options(**ehConf).load() to create the streaming DataFrame. The
'eventhubs' format is provided by the azure-eventhubs-spark connector library.
A cast('string') on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs
NEW QUESTION # 55
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?
Answer: B
Explanation:
Auto Loader incrementally detects and processes new files arriving in Azure Data Lake Storage Gen2 through the cloudFiles Structured Streaming source. It supports near-real-time ingestion while automatically tracking processed files, reducing the custom state-management code required. Its output can be written to a managed Delta table, and built-in schema inference and evolution reduce ongoing maintenance. Scheduled Spark batch jobs introduce latency based on their schedule and usually require custom file-tracking logic. An external table over CSV files does not ingest the data into a managed Delta table. Azure Data Factory can orchestrate ingestion, but it introduces another service and more configuration than the native Databricks capability needed here. Auto Loader is therefore the most direct and maintainable solution for continuously arriving cloud files. Microsoft Learn
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
* 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: |