QUESTION: 149

You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

Remove training examples of high-performing subgroups, and retrain the model.
Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model
Remove the features that have the highest correlations with the majority class.
Upsample or reweight your existing training data, and retrain the model
Redeploy the model, and provide a label explaining the model's behavior to users.

Answer(s): B,D

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QUESTION: 150

You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In the dataset, 96% of examples don’t have the logo, so the dataset is very skewed. Which metric would give you the most confidence in your model?

Precision
Recall
RMSE
F1 score

Answer(s): D

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QUESTION: 151

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Answer(s): C

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QUESTION: 152

You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

Use sparse representation in the test set.
Randomly redistribute the data, with 70% for the training set and 30% for the test set
Apply one-hot encoding on the categorical variables in the test data
Collect more data representing all categories

Answer(s): B

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Free Professional Machine Learning Engineer Exam Braindumps (page: 38)

QUESTION: 149

QUESTION: 150

QUESTION: 151

QUESTION: 152