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| Section | Weight | Objectives |
|---|---|---|
| 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
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Prepare and process data | 30-35% | - Data transformation and modeling
|
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43. Frage
You need to ingest real-time IoT data into Delta Lake with exactly-once guarantees. Which approach should you use?
Antwort: A
Begründung:
Structured Streaming with checkpointing ensures fault tolerance and exactly-once processing semantics in Databricks. It tracks processed offsets and recovers from failures automatically.
Batch ingestion cannot guarantee real-time processing. Copy activity is not designed for streaming workloads and manual ingestion is not scalable or reliable.
44. Frage
You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments. What should you use?
Antwort: A
Begründung:
The best option to use is the Databricks CLI.
The Databricks CLI is the core orchestration tool natively designed to validate, deploy, and manage Declarative Automation Bundles (formerly known as Databricks Asset Bundles) across multiple target environments using simple terminal commands like databricks bundle deploy.
Incorrect:
[Not A]
Git folders: Git folders are strictly a mechanism for version control, code syncing, and repository collaboration inside the workspace. While they are highly integrated with bundle development, they cannot directly deploy or provision bundle targets to isolated environments on their own without the CLI.
[Not C]
Databricks SDK for Python: While the Python SDK provides general programmatic access to Databricks APIs, it is not the primary or optimized tool for executing bundle operations. The bundle framework natively relies on the Databricks CLI executable to compile and translate your configurations.
[Not D]
Jobs UI: The Jobs user interface is built for manual interactions. It does not support native bundle multi-environment targeting or automated, repeatable CI/CD pipelines.
Reference:
https://docs.databricks.com/aws/en/dev-tools/bundles/
45. Frage
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?
Antwort: A
Begründung:
To incorporate changes from your feature branch into the main branch while keeping every individual commit intact, you must use a fast-forward merge or a standard merge commit.
In the Azure Databricks Git folders UI or via standard Git operations, use the following command:
The Correct Command: git merge <branch-name>
Preserves History: Unlike a squash merge, a standard merge keeps all individual commit messages, authors, and timestamps.
Maintains Timeline: It seamlessly integrates the exact commit graph from your feature branch into the main branch.
Reference:
https://docs.databricks.com/aws/en/repos/git-operations-with-repos
46. Frage
You have an Azure Databricks workspace named Workspace1 that contains a Git folder linked to a remote Git repository. The Git folder contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch1 and commit changes to Notebook1.
Another user commits changes to Notebook1 in main.
When you attempt to merge Branch1 into main, a merge conflict occurs.
You need to ensure that Notebook1 in main includes the changes from both branches.
What should you do?
Antwort: A
Begründung:
Branch1 must first incorporate the current changes from main. During that merge, the conflicting sections of Notebook1 can be reviewed and resolved so that the resulting feature-branch version contains the required work from both branches. After committing the resolution, Branch1 can be merged into main normally.
Cloning either branch into another Git folder creates another working copy but does not resolve the conflicting histories. Applying changes directly to main bypasses the controlled feature-branch workflow and risks omitting or overwriting one contributor's work. Resolving the conflict on Branch1 also allows the combined notebook to be tested before updating the shared main branch. Therefore, merging main into Branch1, resolving the conflict, and completing the final merge is the correct workflow.
47. Frage
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?
Antwort: A
Begründung:
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
48. Frage
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