Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A.Area Under the ROC Curve (AUC)
• B.Recall
• C.Misclassification rate
• D.Mean absolute percentage error (MAPE)

Comment: *

Name: *

Email: *

Verification: *

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

• A.Set the early stopping parameter to TRUE
• B.Change the search algorithm from Bayesian search to random search.
• C.Decrease the range of floating-point values
• D.Decrease the maximum number of trials during subsequent training phases.
• E.Decrease the number of parallel trials

Comment: *

Name: *

Email: *

Verification: *

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

• A.Initialize the model with pre-trained weights in all layers including the last fully connected layer.
• B.Initialize the model with random weights in all layers including the last fully connected layer.
• C.Initialize the model with random weights in all layers and replace the last fully connected layer.
• D.Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Comment: *

Name: *

Email: *

Verification: *

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A.Create a Managed Instance Group with autoscaling
• B.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
• C.Create a cluster on Dataproc for training
• D.Use Al Platform for distributed training

Comment: *

Name: *

Email: *

Verification: *

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

• A.Recurrent Neural Networks (RNN)
• B.Reinforcement Learning
• C.Classification
• D.Convolutional Neural Networks (CNN)

Comment: *

Name: *

Email: *

Verification: *