You deployed a conversational application that uses Gemini. You recently collected thousands of rows of user feedback about the verbosity and accuracy of the model's responses. The feedback indicated that the responses are factually correct but users want different levels of verbosity depending on the type of question. You need to adjust the model to return responses that are more consistent with users' expectations. You want to use a scalable solution and minimize application maintenance. What should you do?

• A. Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine-tuning (SFT) in Agent Platform. Re- evaluate the verbosity of responses with the tuned version.
• B. Experiment with other models in Model Garden. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results.
• C. Implement a keyword-based routing layer. If the user's input contains the words "detailed" or
  "description," return a verbose response. If the user's input contains the word "fact," re-prompt Gemini to summarize the response and return a concise response.
• D. Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts.

You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML mode?

• A. Federated learning
• B. Data Loss Prevention API
• C. MD5 to encrypt data
• D. Differential privacy

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

• A. Increase the number of epochs
• B. Increase the learning rate
• C. Increase the batch size
• D. Decrease the learning rate

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 log_time_series_metrics function to track the preprocessed data, and use the log_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. 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.
• D. 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.

You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery.
You want to minimize the serving latency as much as possible. What should you do?

• A. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
• B. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
• C. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
• D. Load the model directly into the Dataflow job as a dependency, and use it for prediction.