Azure DataBricks Assessment Test

An Azure Databricks Assessment Test evaluates candidates for their proficiency in leveraging Azure Databricks, a unified analytics platform built on top of Apache Spark, for big data processing, analytics, and machine learning tasks. Azure Databricks provides an integrated environment for data engineers, data scientists, and analysts to collaborate and work with large-scale datasets efficiently.

An Azure Databricks Assessment Test evaluates candidates for:

1. Understanding of big data concepts, including distributed computing, parallel processing, data partitioning, data storage formats (e.g., Parquet, Avro, ORC), and data ingestion techniques, for processing and analyzing large volumes of data effectively.
2. Proficiency in using Azure Databricks notebooks, clusters, and workspaces for interactive data exploration, data preparation, model development, and visualization tasks, to derive insights and make data-driven decisions.
3. Skills in working with Apache Spark APIs and programming languages such as Python, Scala, or SQL, for performing data manipulation, transformation, aggregation, and analysis operations on distributed datasets within Azure Databricks notebooks.
4. Experience in leveraging Apache Spark SQL for querying structured and semi-structured data, creating and managing temporary and global views, executing SQL queries, and optimizing query performance using techniques like partition pruning and predicate pushdown.
5. Knowledge of machine learning concepts and algorithms, including regression, classification, clustering, feature engineering, hyperparameter tuning, and model evaluation techniques, for building predictive analytics and machine learning models using Azure Databricks MLlib or MLflow.
6. Familiarity with data engineering tasks, such as data cleaning, data wrangling, feature engineering, data pipeline orchestration, and ETL (Extract, Transform, Load) processes, using Azure Databricks and other Azure services like Azure Data Factory and Azure Synapse Analytics.
7. Proficiency in integrating Azure Databricks with other Azure services and tools, such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, Azure Cosmos DB, Azure Key Vault, Azure Active Directory (AAD), Azure DevOps, and Azure Monitor, for end-to-end data analytics workflows.
8. Skills in optimizing and tuning Apache Spark jobs and clusters for performance, scalability, reliability, and cost-efficiency, using techniques like cluster sizing, dynamic allocation, shuffle partitioning, caching, and memory management within Azure Databricks.
9. Experience in collaborating and sharing code, notebooks, and visualizations with team members using Azure Databricks collaborative features, such as workspace sharing, notebook revision history, version control, and collaboration with Git repositories.
10. Knowledge of security best practices for securing data, notebooks, clusters, and workspaces within Azure Databricks, including data encryption, access control, identity management, network security, audit logging, and compliance standards (e.g., GDPR, HIPAA, SOC 2).

Overall, our Azure Databricks Assessment Test evaluates candidates' knowledge, skills, and experience in using Azure Databricks for big data analytics, machine learning, and data engineering tasks, providing valuable insights into their capabilities as data professionals.