some scenario-based questions that can help you assess or practice your knowledge of Azure Databricks

some scenario-based questions that can help you assess or practice your knowledge of Azure Databricks:

### Scenario 1: Data Ingestion and Transformation

**Scenario:**

You are a data engineer at a retail company. The company has a large amount of transaction data stored in Azure Blob Storage and needs to be processed and transformed for analysis. You have been tasked with setting up an Azure Databricks environment to handle this data.

**Questions:**

1. **Data Ingestion:**

   - How would you set up an Azure Databricks cluster to read data from Azure Blob Storage?

   - What are the different methods you can use to read data from Blob Storage in Databricks, and what are the pros and cons of each method?

2. **Data Transformation:**

   - The transaction data is in JSON format. How would you read this JSON data into a DataFrame and perform basic transformations like filtering and aggregating?

   - How would you handle large JSON files to ensure efficient processing in Databricks?

3. **Data Storage:**

   - After processing, you need to store the transformed data in Delta Lake. What are the steps to write the DataFrame to Delta Lake?

   - How would you ensure that the data in Delta Lake is optimized for query performance?

### Scenario 2: Machine Learning Model Deployment

**Scenario:**

You are a data scientist at a financial services company. You have developed a machine learning model to predict stock prices using historical data. The model is built using Python and needs to be deployed in production using Azure Databricks.

**Questions:**

1. **Model Training:**

   - How would you set up an Azure Databricks notebook to train your machine learning model using historical stock price data stored in Azure Data Lake Storage?

   - What are the best practices for managing and versioning your machine learning models in Databricks?

2. **Model Deployment:**

   - How would you deploy the trained model as a REST API endpoint using Databricks?

   - What are the steps to create a Databricks Serving endpoint for your model?

3. **Model Monitoring:**

   - How would you monitor the performance of the deployed model?

   - What tools or features in Databricks can you use to track model performance and ensure it remains accurate over time?

### Scenario 3: Real-Time Data Processing

**Scenario:**

You are a data engineer at a social media company. The company needs to process real-time data from user interactions and store it for analysis. You have been tasked with setting up a real-time data processing pipeline using Azure Databricks.

**Questions:**

1. **Real-Time Data ingestion:**

   - How would you set up an Azure Databricks cluster to ingest real-time data from an event hub?

   - What are the key configurations you need to consider for real-time data processing in Databricks?

2. **Data Processing:**

   - How would you process the real-time data to extract meaningful insights, such as user engagement metrics?

   - How would you handle late-arriving data in your real-time processing pipeline?

3. **Data Storage and Analysis:**

   - After processing, how would you store the real-time data in Delta Lake for further analysis?

   - How would you optimize the storage and query performance for real-time data in Delta Lake?

### Scenario 4: Cost Optimization

**Scenario:**

You are an Azure Databricks administrator at a large enterprise. Your task is to optimize the cost of running Databricks clusters while ensuring high performance and reliability.

**Questions:**

1. **Cluster Management:**

   - How would you configure Databricks clusters to use Azure Spot VMs for cost savings?

   - What are the best practices for managing cluster lifecycles to reduce costs?

2. **Resource Utilization:**

   - How would you monitor and optimize resource utilization in Databricks clusters?

   - What are the tools or features in Databricks that can help you identify and address resource bottlenecks?

3. **Cost Monitoring:**

   - How would you set up cost monitoring and alerts for Databricks clusters?

   - What are the best practices for regular cost reviews and adjustments in Databricks?

These scenarios and questions cover a range of topics from data ingestion and transformation to machine learning deployment and real-time data processing, as well as cost optimization. They are designed to help you apply your knowledge in practical, real-world situations.

Understanding Microsoft Fabric Capacity: Workspaces, Licensing, and Performance Optimization

 Fabric Capacity:

Definition: A Fabric capacity is essentially a pool of compute and storage resources to execute workloads. These capacities are required for running Microsoft Fabric features like reports, data pipelines, and notebooks.

Azure-Based Capacities: These capacities are managed under an Azure subscription. Capacities can be created via the Azure portal, offering flexibility in managing resources and costs.

Sizes: Capacities come in various sizes, from smaller capacities like F2 for experimentation to larger capacities like F64, which align with Power BI Premium features. Pricing varies by capacity size, geography, and currency.

Fabric Workspaces:

Definition: Workspaces act as logical containers where projects, workloads, and items like datasets, reports, and pipelines are organized.

Assignment to Capacity: Each workspace must be assigned to a specific capacity. Without this assignment, items in the workspace cannot execute.

Licensing Model:

Pay-As-You-Go (F License): The "pay-as-you-go" model allows you to pay for capacity usage based on consumption. This model provides flexibility, enabling you to scale resources up or down or pause capacity when not in use.

Yearly Licensing Model: This includes Power BI Premium, where capacities are pre-purchased for a fixed term. This model doesn't allow scaling or pausing but may offer cost benefits for consistent usage.

Smoothing and Bursting:

Smoothing: Ensures that workloads are distributed evenly over time to avoid spikes in capacity usage. Interactive tasks are smoothed over minutes, while large background jobs are smoothed over 24 hours.

Bursting: Allows temporary capacity increases for heavy workloads without immediate capacity resizing. This ensures critical tasks complete successfully without hitting capacity limits.

• Smoothing spreads out workloads over time to avoid capacity spikes.
• Bursting temporarily increases capacity for resource-intensive tasks to ensure they complete without hitting limits.

Storage and Additional Costs:

Storage Costs: In addition to compute, organizations are charged for storage (e.g., data lake storage costs).

User Licenses: Users require appropriate licenses:

For consuming reports: Power BI Free or Pro licenses.

For creating reports or items in Fabric: Power BI Pro licenses.

Basic Understanding

What is the purpose of assigning a Fabric workspace to a capacity, and what happens if it is not assigned?

  Can multiple workspaces share a single Fabric capacity? What are the benefits of this approach?

Licensing and Billing

How does the "Pay-As-You-Go" licensing model differ from the yearly licensing model in Microsoft Fabric?

What is the advantage of pausing a capacity in the pay-as-you-go model, and how does it reduce costs?

Azure Integration

How does managing Fabric capacities under Azure subscriptions benefit organizations in terms of cost management and scalability?

If a heavy task exceeds the available Fabric capacity, how does the "bursting" mechanism ensure its completion?

Performance Optimization

Explain the concept of "smoothing" in Fabric workloads. How does it help optimize resource usage?

How would you monitor and resize a Fabric capacity to handle increasing workloads?

Governance and Security

How does associating capacities with Azure subscriptions improve governance and billing transparency?

What considerations should be made when deciding on the size of a Fabric capacity for an organization?

Advanced Scenarios

Describe a scenario where resizing or pausing a Fabric capacity might be necessary.

How does the licensing requirement differ between small and large Fabric capacities when consuming Power BI reports?

Storage and Data

Apart from compute capacity, what additional costs must be considered when using Fabric?

How do storage costs for Fabric vary, and what pricing model is used for data storage?