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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data ingestion
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
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NEW QUESTION # 41
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?
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
NEW QUESTION # 42
Case Study 1 - Contoso, Inc.
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
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 Data Environment Contoso ingests the following operational and business data:
- Telemetry data: More than 40,000 IoT 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 IoT 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.
Requirements
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 (BI) 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.
- 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.
Drag and Drop Question
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:
NEW QUESTION # 43
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 # 44
You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?
Answer: B
Explanation:
Lakeflow Jobs provides native orchestration for multi-task Databricks workflows. Separate ingestion, cleansing, and curation tasks can be connected through explicit dependencies, ensuring that each stage starts only after its required upstream work succeeds. Each task can also have independent retry, notification, timeout, and compute settings, directly addressing the pipeline's resiliency requirements. Azure Data Factory could orchestrate notebooks, but it introduces another service when Lakeflow Jobs already provides the required functionality. A single notebook makes failures harder to isolate and can force successful stages to be rerun. Independently scheduled jobs rely on timing assumptions rather than actual task completion and can fail when an upstream stage runs longer than expected. Explicit Lakeflow Jobs dependencies provide reliable execution order and centralized monitoring. Microsoft Learn
NEW QUESTION # 45
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
Answer: D
Explanation:
The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn
NEW QUESTION # 46
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