Exam Professional Machine Learning Engineer topic 1 question 7 discussion

A. Because BigQuery ML need to write code.

create a model without doing literally anything, logo AutoML. A.

A) Auto ML Tables doesn’t require code.

The question says 'over several structured datasets' means large/multiple datasets and 'several times' means frequently use of data. Though BigQuery ML is not an absolute 'NO Code' solution but all it needs is very simple SQL query to train ML model So 'B' could be the correct answer here but it is asking for Hyperparameter tuning which is not available in BigQuery ML so correct answer is 'A'

A - AutoML is no code

requirement : No code A. Configure AutoML Tables to perform the classification task. : No code B. Run a BigQuery ML task to perform logistic regression for the classification. : coding LR model C. Use AI Platform Notebooks to run the classification model with pandas library. : Notebooks include codes D. Use AI Platform to run the classification model job configured for hyperparameter tuning.: job needs to be written what to execute

Went with A

Because BigQueryML doesn't have lots of steps that mentioned in question

"without writing code" ==> AutoML A is correct

Correct answer is "A"

Correct answer is "A"

Because BigQuery ML need to write code, so A is the correct one

"without writing code" only A option complies with this statment , all other options requires writing code

A is correct

A is correct https://cloud.google.com/automl-tables/docs/beginners-guide

=New Question7= You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do? A. Build your custom container to run jobs on Al Platform Training B. Use a built-in model available on Al Platform Training C. Build your custom containers to run distributed training jobs on Al Platform Training D. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

You have to export out BQ trained ML model to set it up for inference. Inference is not natively offered in BQ. You can perform EDA in autoML tables.