Machine Learning with BigQuery Practice Exam
Machine Learning with BigQuery Practice Exam
About Machine Learning with BigQuery Practice Exam
The Machine Learning with BigQuery exam evaluates your proficiency in leveraging Google BigQuery's machine learning capabilities to analyze data and develop predictive models directly within a data warehouse environment. This exam tests your ability to harness BigQuery ML's SQL-based interface for tasks such as data preprocessing, feature engineering, and creating, training, and deploying machine learning models without extensive coding.
Skills Required
- Strong understanding of SQL for querying, data manipulation, and aggregation in BigQuery.
- Experience with analytical functions and advanced SQL techniques.
- Comprehensive knowledge of BigQuery architecture, features, and data warehouse functionalities.
- Familiarity with partitioning, clustering, and optimizing queries for performance.
- Understanding core ML concepts, including supervised and unsupervised learning, regression, classification, and clustering.
- Ability to evaluate model performance using metrics like accuracy, precision, recall, and RMSE.
- Proficiency in creating and training ML models directly in BigQuery using SQL (e.g., linear regression, logistic regression, k-means clustering, and time-series forecasting).
Knowledge Required
- Knowledge of feature engineering, data preprocessing, and hyperparameter tuning within BigQuery ML.
- Skills to interpret model outputs and derive actionable insights.
- Experience deploying ML models for batch predictions or integrating with external applications.
- Familiarity with connecting BigQuery to visualization tools like Looker, Tableau, or Google Data Studio.
- Ability to generate reports and dashboards showcasing predictive analytics outcomes.
- Knowledge of BigQuery APIs, Python integration, and using cloud tools like Cloud Storage and Dataflow for extended ML workflows.
- Awareness of data security, encryption, and compliance standards while working with sensitive data in BigQuery.
- Analytical thinking to identify business problems and effectively apply BigQuery ML solutions.
Who should take the Exam?
The Machine Learning with BigQuery exam is ideal for:
- Data Analysts and Data Scientists
- Data Engineers
- Machine Learning Practitioners
- Business Intelligence Specialists
- Cloud Professionals and Architects
- Developers and Software Engineers
- Managers and Decision-Makers
- Students and Enthusiasts
Course Outline
The Machine Learning with BigQuery Exam covers the following topics -
Domain 1 - Introduction
- Overview of the Course
- The Concept of Scaling Out Instead of Up
- Google's Scaled-Out Data Revolution
- Demo: Setting Up an Account on Google Cloud Platform
Domain 2 - BigQuery Basics
- Understanding BigQuery: Definition and Capabilities
- How BigQuery Stores Structured Data
- Benefits of Parallel Execution
- Demo: Navigating the BigQuery Web UI
- Clarifying What BigQuery Is Not
- Overview of BigQuery’s Technology Stack
- Demo: Basic Navigation Techniques
Domain 3 - Introduction to Applied Machine Learning
- Core Concepts of Machine Learning
- Exploring Three Core Machine Learning Careers
- The Applied Machine Learning Workflow
- Types of Machine Learning (Supervised, Unsupervised, etc.)
- The Importance of Python in Machine Learning
- Setting Up Python:
- Windows Installation
- MAC Installation
- Introduction to Arrays
- Navigating Jupyter Notebooks for Data Science
Domain 4 - Machine Learning Libraries
- Overview of Core Machine Learning Libraries
- Demo: Using Core Machine Learning Libraries
- Sourcing and Preparing Data
- Exploratory Data Analysis and Insights
- Data Cleansing and Preprocessing Techniques
- Demo: Building and Evaluating Machine Learning Models
Domain 5 - Classification and Regression
- Introduction to Linear Regression Techniques
- Demo: Linear Regression in Action
- Fundamentals of Classification Models
- Demo: Implementing Classification Models
- Exploring Artificial Neural Networks and Their Applications
Domain 6 - Machine Learning with BigQuery
- Managing Datasets and Tables in BigQuery
- Demo: Working with Datasets and Tables
- Demo: Using Cloud Datalab for Machine Learning
- Titanic Dataset Modeling
- Iris Dataset Modeling
- Scaling Cloud Datalab for Large-Scale Analysis
- Introduction to BigQuery ML
- Demo: Binary Logistic Regression with BigQuery ML
- Setting Up the Google Cloud SDK
- Demo: Navigating with gsutil
- Demo: Segmenting Datasets for Advanced Analytics
