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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| 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 # 11
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluster1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
Answer: B
Explanation:
The best action is uploading the libraries to the workspace and installing the libraries on the cluster (or ideally uploading them to Unity Catalog volumes).
Unity Catalog Compatibility: When using Unity Catalog for access control, compute clusters are typically configured with Standard (Shared) access mode. In this mode, traditional cluster init scripts [Not B.] face strict execution restrictions or are completely blocked to maintain secure user isolation.
Governance: Uploading your packages as Workspace Files or to Unity Catalog volumes allows administrators to manage access permissions directly and add them to an allowlist if needed.
Cluster-Wide Availability: Installing the libraries via the cluster's Libraries tab ensures that the required ingestion packages are automatically pre-installed and available across all nodes and notebooks running on that cluster.
Incorrect:
[Not A]
Running pip3 install manually on a cluster terminal or inside a notebook only applies to the specific notebook session (notebook-scoped). It does not natively persist across cluster restarts or handle cross-node execution effectively for data ingestion pipelines.
[Not B]
Running a custom script or a legacy init script to modify system-level paths introduces security risks and is generally incompatible with Unity Catalog's strict execution isolation policies for shared compute.
Reference:
https://docs.databricks.com/aws/en/libraries/
NEW QUESTION # 12
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 # 13
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
- Deleted data must be retained for 30 days to support audits.
- Deleted data that is older than 30 days must be removed permanently.
- The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
To configure an Azure Databricks managed Delta table to retain deleted data for 30 days and minimize administrative overhead, you must set the following table properties:
delta.logRetentionDuration: Set this to interval 30 days. This property controls how long the transaction log history is kept, which is essential for audit trails and time travel.delta.
deletedFileRetentionDuration: Set this to interval 30 days. This property determines the threshold for when deleted data files become eligible for permanent removal by the VACUUM command.
Reference:
https://docs.databricks.com/aws/en/delta/history
NEW QUESTION # 14
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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