Google Professional Machine Learning Engineer Exam Questions

Are you preparing for your Google Professional Machine Learning Engineer Exam? PassQuestion provides the latest Google Professional Machine Learning Engineer Exam Questions to help you best prepare for your test, you can practice 60 online questions and answers so that you can pass your Google Professional Machine Learning Engineer exam easily.

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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?

 
 
 
 

2. You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns .

How should you ensure that AutoML fits the best model to your data?

 
 
 
 

3. You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices .

How should you configure the end-to-end architecture of the predictive model?

 
 
 
 

4. You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables .

What should you do?

 
 
 
 

5. You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12

hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage .

How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

 
 
 
 

6. You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard .

What should you do?

 
 
 
 

7. You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code .

What should you do?

 
 
 
 

8. You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness .

Which actions should you take? Choose 2 answers

 
 
 
 
 

9. You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production .

What is the most streamlined and reliable way to perform this validation?

 
 
 
 

10. You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model .

What should you do?

 
 
 
 

11. You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting .

Which strategy should you use when retraining the model?

 
 
 
 

12. You work for an advertising company and want to understand the effectiveness of your company’s latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook .

What should you do?

 
 
 
 

13. 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?

 
 
 
 

14. During batch training of a neural network, you notice that there is an oscillation in the loss .

How should you adjust your model to ensure that it converges?

 
 
 
 

15. You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model’s accuracy dropped to 66% .

How can you make your production model more accurate?

 
 
 
 

16. You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge .

How should you resolve the class imbalance problem?

 
 
 
 

17. Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data .

How should you address the input differences in production?

 
 
 
 

18. You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories .

What should you do?

 
 
 
 

19. You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential

customers .

What factors should you consider before building the model?

 
 
 
 

20. Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time .

What should they use to track and report their experiments while minimizing manual effort?

 
 
 
 

21. You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.

What should you do?

 
 
 
 

22. You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn’t changed; however the accuracy of the model has steadily deteriorated.

What issue is most likely causing the steady decline in model accuracy?

 
 
 
 

23. You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.

What should you do?

 
 
 
 

24. You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production.

What is the most streamlined and reliable way to perform this validation?

 
 
 
 

25. 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?

 
 
 
 

26. You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory.

How should you create a dataset following Google-recommended best practices?

 
 
 
 

27. You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible.

What should you do?

 
 
 
 

28. You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables.

What should you do?

 
 
 
 

29. Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data.

How should you address the input differences in production?

 
 
 
 

30. You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure.

Which additional readiness check should you recommend to the team?

 
 
 
 

31. You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time.

How should you build the model?

 
 
 
 

32. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

33. You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world.

Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

 
 
 
 

34. You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime.

How should you perform this comparison?

 
 
 
 

35. You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction.

Which architecture should you use?

 
 
 
 

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