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| Section | Weight | Objectives |
|---|---|---|
| Prepare and process data | 30-35% | - Data quality and validation
|
| 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
|
>> New DP-750 Test Objectives <<
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NEW QUESTION # 79
What is the best way to reduce Databricks compute cost for intermittent workloads?
Answer: C
Explanation:
Autoscaling adjusts resources based on workload demand, and auto-termination shuts down idle clusters, reducing cost significantly. All-purpose clusters are always on. Increasing size raises cost. Single-node clusters limit scalability and are not suitable for production workloads.
NEW QUESTION # 80
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.
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?
Answer: C
Explanation:
Enabling Photon acceleration on a job compute cluster is the best option. It significantly speeds up data engineering pipelines, handles JSON file ingestion seamlessly, and optimizes cost- efficiency for bursty production workloads, which are key for processing rapidly arriving IoT sensor events.
Enable Photon acceleration for a job compute cluster:
Databricks Auto Loader is designed for low-latency, high-volume file ingestion. Running Auto Loader on a job compute cluster provides the lowest operational cost because job clusters are billed at a much lower rate than all-purpose clusters.
Enabling Photon acceleration heavily optimizes the ingestion, parsing, and processing of raw JSON data strings, allowing the cluster to handle bursty workloads faster and auto-scale more efficiently.
Scenario, 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.
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.
Reference:
https://medium.com/@krthiak/10-days-of-data-engineering-interview-qna-day-5-db1c0b58bf86
NEW QUESTION # 81
What does the VACUUM command do in Delta Lake?
Answer: C
Explanation:
VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.
NEW QUESTION # 82
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
* Ensure that amount is always greater than 0.
* Fail the pipeline update for Table1 when data that violates rule1 is detected.
Which statement should you execute?
Answer: A
Explanation:
The expect_or_fail decorator validates every incoming record against the expression amount > 0 and stops the table update when a violation occurs. This exactly meets the requirement that invalid data cause the pipeline update to fail. Option A applies multiple expectations and drops violating records instead of failing the update. Option B also discards invalid records while allowing valid records to continue processing. Option D records expectation metrics but retains invalid records and allows the update to continue. These alternative behaviors are useful when invalid data can be tolerated, quarantined, or monitored, but they do not satisfy strict enforcement. The fail action provides transactional protection because the affected update does not successfully publish data that violates the stated quality rule.
NEW QUESTION # 83
You use Databricks Asset Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements:
- Deploy the app to both environments.
- Deploy only one job to development.
- Minimize administrative effort.
What should you use?
Answer: D
Explanation:
To meet your deployment needs with minimal administrative overhead, you should use Databricks Asset Bundle (DAB) targets combined with resource overrides or conditional lookups using Go template syntax in your databricks.yml file.
Instead of creating separate bundles or complex CI/CD logic, you can declare both jobs and the app inside the main resources block, and then filter or modify them per target environment.
Implementation Options
Depending on your preference for keeping configuration declarative or fully conditional, choose one of the two standard patterns:
*-> Option 1: Resource Overrides via targets (Recommended)
Define all components globally, but use the targets block to remove or empty out the configuration of the job you want to skip in development.yamlbundle:
name: my-unified-bundle
apps:
my_app:
# App configurations go here...
jobs:
job_shared:
# Configurations for the job deployed to BOTH dev and prod...
job_prod_only:
name: Only for Production
# Production configurations...
targets:
dev:
mode: development
workspace:
host: https://dev-workspace.cloud.databricks.com
# Keep administrative work low by overriding the prod job to null/empty resources:
jobs:
job_prod_only: !null
prod:
mode: production
workspace:
host: https://prod-workspace.cloud.databricks.com
Option 2: Go Template Conditional LogicIf you prefer not to use !null overrides, you can evaluate the target environment dynamically at deployment time using standard Go templates.
Reference:
https://docs.databricks.com/aws/en/dev-tools/bundles/workspace-deploy
NEW QUESTION # 84
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