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WorksheetsMachine Learning Basics V1
Total questions: 10
Worksheet time: 20mins
You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?
Increase the learning rate.
Increase the number of epochs.
Decrease the learning rate.
Increase the batch size.
How should you adjust the training parameters in AutoML to improve model performance? (Choose two.)
Increase the score threshold.
Decrease the score threshold.
Add more positive examples to the training set.
Add more negative examples to the training set.
Reduce the maximum number of node hours for training.
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?
Train your model in a distributed mode using multiple Compute Engine VMs.
Train your model using Vertex AI Training with CPUs.
Migrate your model to TensorFlow, and train it using Vertex AI Training.
Train your model using Vertex AI Training with GPUs.
Which model should you choose?
The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5.
The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
The model with the highest recall where precision is greater than 0.5.
The model with the highest precision where recall is greater than 0.5.
Your team has a limited budget and is already overextended. What should you do?
Add synthetic training data where those phrases are used in non-toxic ways.
Remove the model and replace it with human moderation.
Replace your model with a different text classifier.
Raise the threshold for comments to be considered toxic or harmful.
You want your model to scale smoothly and require minimal development work. What should you do?
Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
Develop a regression model using BigQuery ML.
Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
. Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.
You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?
Verify that your model can obtain a low loss on a small subset of the dataset.
Add handcrafted features to inject your domain knowledge into the model.
Use the Vertex AI hyperparameter tuning service to identify a better learning rate.
Use hardware accelerators and train your model for more epochs.
What should you do?
Train a TensorFlow model on Vertex AI.
Train a classification Vertex AutoML model.
Run a logistic regression job on BigQuery ML.
Use scikit-learn in Vertex AI Workbench user-managed notebooks with pandas library.
You want to decrease training time without sacrificing model performance. What should you do?
Increase the instance memory to 512 GB, and increase the batch size.
Replace the NVIDIA P100 GPU with a K80 GPU in the training job.
Enable early stopping in your Vertex AI Training job.
Use the tf.distribute.Strategy API and run a distributed training job.
You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?
F1 score
Recall
Accuracy
Precision
