Databricks-Certified-Data-Analyst-Associate

The Databricks Certified Data Analyst Associate exam assesses your understanding of fundamental data analysis concepts and how they apply to Databricks. Here's a breakdown of the main points covered:

1. Data Exploration and Transformation:

• Data Ingestion and Loading:
  • Understanding different data sources and formats (CSV, JSON, Parquet, etc.)
  • Using Databricks utilities like spark.read and spark.write to load data into Spark DataFrames.
• Data Cleaning and Transformation:
  • Detecting and handling missing or inconsistent data.
  • Applying data transformations like filtering, aggregation, joining, and window functions.
  • Utilizing Spark SQL and DataFrame operations for data manipulation.
• Data Exploration and Visualization:
  • Using SQL queries and DataFrame methods to explore data and identify patterns.
  • Creating basic visualizations with tools like Matplotlib or plotting libraries within Databricks.

2. Data Warehousing and Analytics:

• Data Modeling and Design:
  • Understanding data warehousing concepts (dimensional modeling, star schemas).
  • Designing data models and tables for efficient analysis.
  • Using Databricks Delta Lake for data versioning and reliability.
• Data Querying and Analysis:
  • Writing efficient SQL queries on Spark DataFrames.
  • Understanding different types of joins and their applications.
  • Applying aggregate functions and window functions for complex analysis.
• Data Visualization and Reporting:
  • Generating insightful reports and dashboards using visualizations.
  • Integrating Databricks with visualization tools like Tableau or Power BI.

3. Databricks Fundamentals:

• Databricks Workspace:
  • Understanding Databricks workspace structure and components.
  • Managing notebooks, clusters, and jobs.
• Spark Fundamentals:
  • Understanding basic Spark concepts like RDDs, DataFrames, and Spark SQL.
  • Using Spark for distributed data processing and analysis.
• Databricks Utilities and Libraries:
  • Familiarizing yourself with Databricks utilities like dbutils and mlflow .
  • Using various Python libraries for data analysis and machine learning.

4. Machine Learning and AI (Basic Concepts):

• Machine Learning Concepts:
  • Understanding basic machine learning concepts like supervised learning, unsupervised learning, and regression/classification.
  • Knowing different types of algorithms (linear regression, decision trees, etc.)
• Machine Learning in Databricks:
  • Understanding how to use Databricks for machine learning tasks.
  • Familiarizing yourself with MLlib library for basic algorithm implementations.

Exam Preparation:

• Databricks Learning Path: Utilize Databricks' official learning path for the Data Analyst Associate certification.
• Practice Exams: Use available practice exams and mock tests to assess your preparedness.
• Hands-on Experience: Work on real-world data analysis projects using Databricks to gain practical experience.

Remember, this is a general overview. The official Databricks Certified Data Analyst Associate exam guide provides a detailed syllabus and specific learning objectives. Refer to that guide for a comprehensive understanding of the exam expectations.