You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

• A.Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
• B.Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
• C.Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.
• D.Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.

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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?

• A.Normalize the data for the training, and test datasets as two separate steps.
• B.Split the training and test data based on time rather than a random split to avoid leakage
• C.Add more data to your test set to ensure that you have a fair distribution and sample for testing
• D.Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

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A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.

Which model would have the HIGHEST accuracy?

• A.Single perceptron with a Tanh activation function
• B.Decision tree
• C.Support vector machine (SVM) with a radial basis function kernel
• D.Linear support vector machine (SVM)

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A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

• A.The model should be periodically retrained using the original training data plus new data as product inventory changes.
• B.The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• C.The model needs to be completely re-engineered because it is unable to handle product inventory changes.
• D.The model's hyperparameters should be periodically updated to prevent drift.

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You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

• A.Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
• B.Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
• C.Use the Al Platform custom containers feature to receive training jobs using any framework
• D.Create a library of VM images on Compute Engine; and publish these images on a centralized repository

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