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 Professional-Machine-Learning-Engineer Dumps with Practice Exam Questions Answers

Questions: 73 questions

Last Update: Aug 8, 2022

Google Certification Exam Professional-Machine-Learning-Engineer has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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Professional-Machine-Learning-Engineer Questions and Answers

Question # 1

You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A.

data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})

B.

data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})

C.

data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})

D.

data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

Question # 2

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

A.

Use the "Other Products You May Like" recommendation type to increase the click-through rate

B.

Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.

C.

Import your user events and then your product catalog to make sure you have the highest quality event stream

D.

Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Question # 3

You started 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?

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.

Question # 4

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

A.

Classification

B.

Reinforcement Learning

C.

Recurrent Neural Networks (RNN)

D.

Convolutional Neural Networks (CNN)

Question # 5

You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

A.

An optimization objective that minimizes Log loss

B.

An optimization objective that maximizes the Precision at a Recall value of 0.50

C.

An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value

D.

An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

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