You built a custom Vertex AI pipeline job that preprocesses images and trains an object detection model. The pipeline currently uses 1 n1-standard-8 machine with 1 NVIDIA Tesla V100 GPU. You want to reduce the model training time without compromising model accuracy. What should you do?

• A.Reduce the number of layers in your object detection model.
• B.Train the same model on a stratified subset of your dataset.
• C.Update the WorkerPoolSpec to use a machine with 24 vCPUs and 1 NVIDIA Tesla V100 GPU.
• D.Update the WorkerPoolSpec to use a machine with 24 vCPUs and 3 NVIDIA Tesla V100 GPUs.

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You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human.
Which metric(s) should you use to monitor the model's performance?

• A.Number of messages flagged by the model per minute
• B.Number of messages flagged by the model per minute confirmed as being inappropriate by humans.
• C.Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
• D.Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

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You have recently developed a custom model for image classification by using a neural network.
You need to automatically identify the values for learning rate, number of layers, and kernel size.
To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management.
What should you do?

• A.Train an AutoML image classification model.
• B.Create a custom training job that uses the Vertex AI Vizier SDK for parameter optimization.
• C.Create a Vertex AI hyperparameter tuning job.
• D.Create a Vertex AI pipeline that runs different model training jobs in parallel.

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Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

• A.The model predicts videos as popular if the user who uploads them has over 10,000 likes.
• B.The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
• C.The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
• D.The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

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You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

• A.Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.
• B.Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.
• C.Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.
• D.Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

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