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  • Exam Name: Google Professional Machine Learning Engineer
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Professional-Machine-Learning-Engineer Questions and Answers

Question # 1

You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?


Extract sentiment directly from the voice recordings


Convert the speech to text and build a model based on the words


Convert the speech to text and extract sentiments based on the sentences


Convert the speech to text and extract sentiment using syntactical analysis

Question # 2

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?


Categorical hinge


Binary cross-entropy


Categorical cross-entropy


Sparse categorical cross-entropy

Question # 3

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?


AutoML Vision model


AutoML Vision Edge mobile-versatile-1 model


AutoML Vision Edge mobile-low-latency-1 model


AutoML Vision Edge mobile-high-accuracy-1 model

Question # 4

Your company manages a video sharing website where users can watch and upload videos. You need to

create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company’s website. Which result should you use to determine whether the model is successful?


The model predicts videos as popular if the user who uploads them has over 10,000 likes.


The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.


The model predicts 95% of the most popular videos measured by watch time within 30 days of being



The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Question # 5

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?


Use Al Platform for distributed training


Create a cluster on Dataproc for training


Create a Managed Instance Group with autoscaling


Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

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