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.