Exam Professional Machine Learning Engineer topic 1 question 252 discussion

Option B because there's no mention of "flexibility". Easy access to viz tools with Looker

Option D provides the best balance of simplicity and meeting all requirements. It allows for visualization and EDA in Vertex AI Workbench and uses the simplest modeling approach (BQML via SQL commands) executed directly from the notebook environment using IPython magics, creating a cohesive and straightforward workflow.

The correct answer is probably B. AutoML does the feature engineering for you, so it requires the least amount of effort. For all other options, you have to do the exploration and feature engineering yourself, which will take a lot of time. In a real world scenario you'd expect to do a bit of EDA to identify and deal with missing values and to check the data quality though... even if you go for the B option.

Going for D

Option D is the answer given by an official Google trainer.

Simple training and integration with visualization tools = BQ

As requested :" simplest approach", the option B is the best choice.

D Vertex AI Workbench notebook: Provides an environment for data analysis, model building, and visualization tools all in one place. IPython magics: Allows seamless interaction with BigQuery for data exploration and feature creation directly within the notebook. CREATE MODEL statement: Enables model creation within the notebook environment, simplifying the workflow. ML.EVALUATE and ML.PREDICT statements: Facilitate model validation directly within the notebook for assessing performance.

B. Use Bigquery ML Features to create, evaluate and predict

As requested :" simplest approach", the option B is the best choice.

Forgot to vote.

Simplest approach that allows visualization is option B.

all the other options create a new BQ table, I don't think it's needed.

Option B: While AutoML simplifies model selection and training, it lacks the flexibility and visualization capabilities of Vertex AI Workbench. Option C: Manually saving features as CSV files and importing them back into BigQuery involves unnecessary data movement and complexity. Option D: Completing all steps within the notebook is possible but requires more coding and might not be as intuitive for those less familiar with BigQuery ML syntax.