Professional Machine Learning Engineer
Professional Machine Learning Engineer
Money Back Guarantee
98% Success Rate
Real Questions
1
Architecting low-code AI solutions 7%
1.1
Developing ML models by using BigQuery ML
3 subtopics1.1.1Building appropriate BigQuery ML models
1.1.2Feature engineering in BigQuery ML
1.1.3Generating predictions
Learning Objectives
- Build appropriate BigQuery ML models based on business problems
- Perform feature engineering and selection using BigQuery ML
- Generate predictions using BigQuery ML
1.2
Building AI solutions using ML APIs or foundation models
3 subtopics1.2.1ML APIs from Model Garden
1.2.2Industry-specific APIs
1.2.3RAG applications
Learning Objectives
- Build applications using ML APIs from Model Garden
- Implement industry-specific APIs
- Create RAG applications using Vertex AI Agent Builder
1.3
Training models using AutoML
3 subtopics1.3.1Data preparation for AutoML
1.3.2Training custom models
1.3.3Forecasting models
Learning Objectives
- Prepare data for AutoML training
- Train custom models using various data types
- Create and configure forecasting models
Domain Hands-on Skills
Creating BigQuery ML modelsUsing Model Garden APIsTraining AutoML models
Common Mistakes to Avoid
- Not understanding when to use BigQuery ML vs AutoML
- Incorrect data preparation for AutoML
- Misunderstanding RAG implementation patterns
2
Collaborating within and across teams to manage data and models 14%
3
Scaling prototypes into ML models 18%
4
Serving and scaling models 20%
5
Automating and orchestrating ML pipelines 22%
6