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You are developing an ML pipeline using Vertex AI Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex AI Model Registry and deploy it to Vertex AI Endpoints for online inference. You want to use the simplest approach. What should you do?

• A. Use the Vertex AI ModelEvaluationOp component to evaluate the model
• B. Use the Vertex AI REST API within a custom component based on a vertex-ai/prediction/xgboost- cpu image
• C. Use the Vertex AI SDK for Python within a custom component based on a python:3.10 image
• D. Chain the Vertex AI ModelUploadOp and ModelDeployOp components together

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?

• A. Use the Cloud Natural Language API to extract custom entities for classification.
• B. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.
• C. Use the AI Platform Training built-in algorithms to create a custom model.
• D. Use AutoMlL Natural Language to extract custom entities for classification.

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. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
• B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
• C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
• D. 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.

You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

• A. 1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
  2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
• B. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values.
• C. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
• D. 1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.

You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

• A. Embed the client on the website, and then deploy the model on AI Platform Prediction.
• B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
• C. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
• D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.