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)

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You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

• A.Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
• B.Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
• C.Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
• D.Schedule a weekly query in BigQuery to compute the success metric.

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A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

• A.Area under the curve
• B.Root Mean Square Error (RMSE)
• C.Residual plots
• D.Confusion matrix

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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?

• A.Create a cluster on Dataproc for training
• B.Use Al Platform for distributed training
• C.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
• D.Create a Managed Instance Group with autoscaling

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A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

• A.Add more deep trees to the random forest to enable the model to learn more features.
• B.Change the cost function so that false positives have a higher impact on the cost value than false negatives.
• C.Change the cost function so that false negatives have a higher impact on the cost value than false positives.
• D.Include a copy of the samples in the test dataset in the training dataset.
• E.Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

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