Free Online Google Professional-Machine-Learning-Engineer Practice Test

Prepare Your Google Professional-Machine-Learning-Engineer Exam Questions with Free online Professional-Machine-Learning-Engineer Practice Test. Get Brilliant Professional Machine Learning Engineer Exam Results with Valid Professional Machine Learning Engineer Exam Dumps.

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Total 298 Questions | Updated On: Jul 16, 2024

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Question 1

You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

A : Compare the loss performance for each model on a held-out dataset.

B : Compare the loss performance for each model on the validation data.

C : Compare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.

D : Compare the mean average precision across the models using the Continuous Evaluation feature.

Answer: B

Question 2

You work on a team in a data center that is responsible for server maintenance. Your management team wants you to build a predictive maintenance solution that uses monitoring data to detect potential server failures. Incident data has not been labeled yet. What should you do first?

A : Train a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.

B : Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Use this heuristic to monitor server performance in real time.

C : Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.

D : Hire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

Answer: D

Question 3

You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A : Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

B : Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew

C : Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

D : Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.

Answer: A

Question 4

You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?

A : Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

B : Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.

C : Use the CREATE MODEL statement and select the categorical and non-categorical features.

D : Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Answer: D

Question 5

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.)

A : Remove training examples of high-performing subgroups, and retrain the model.

B : Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model

C : Remove the features that have the highest correlations with the majority class.

D : Upsample or reweight your existing training data, and retrain the model

E : Redeploy the model, and provide a label explaining the model's behavior to users.

Answer: B,D

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Total 298 Questions | Updated On: Jul 16, 2024

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