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Exactprep Professional-Machine-Learning-Engineer Questions

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Question 40

You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

Options:

A.

Use Vertex AI Workbench user-managed notebooks to generate the report.

B.

Use the Google Data Studio to create the report.

C.

Use the output from TensorFlow Data Validation on Dataflow to generate the report.

D.

Use Dataprep to create the report.

Question 41

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

Options:

A.

Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.

B.

Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.

C.

Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.

D.

Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

Question 42

You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

Options:

A.

Convert each categorical value into an integer value.

B.

Convert the categorical string data to one-hot hash buckets.

C.

Map the categorical variables into a vector of boolean values.

D.

Convert each categorical value into a run-length encoded string.

Question 43

You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

Options:

A.

Address the model overfitting by using a less complex algorithm.

B.

Address data leakage by applying nested cross-validation during model training.

C.

Address data leakage by removing features highly correlated with the target value.

D.

Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Page: 10 / 21
Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 22, 2024
Questions: 285
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