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Online Professional-Machine-Learning-Engineer Questions Video

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Question 28

You have been given a dataset with sales predictions based on your company’s marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

Options:

A.

Use BigQuery ML to run several regression models, and analyze their performance.

B.

Read the data from BigQuery using Dataproc, and run several models using SparkML.

C.

Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.

D.

Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

Question 29

You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.

You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

Options:

A.

The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5

B.

The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.

C.

The model with the highest recall where precision is greater than 0.5.

D.

The model with the highest precision where recall is greater than 0.5.

Question 30

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

Options:

A.

Configure a Compute Engine instance with Spark and use Jupyter notebooks.

B.

Set up a Dataproc cluster with Spark and use Jupyter notebooks.

C.

Set up a Vertex AI Workbench instance with a Spark kernel.

D.

Use Colab Enterprise with a Spark kernel.

Question 31

Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

Options:

A.

Add synthetic training data where those phrases are used in non-toxic ways

B.

Remove the model and replace it with human moderation.

C.

Replace your model with a different text classifier.

D.

Raise the threshold for comments to be considered toxic or harmful

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