You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

• A.Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
• B.Compare the loss performance for each model on a held-out dataset.
• C.Compare the loss performance for each model on the validation data
• D.Compare the mean average precision across the models using the Continuous Evaluation feature

Comment: *

Name: *

Email: *

Verification: *

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

• A.Rewrite your input function to resize and reshape the input images.
• B.Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
• C.Rewrite your input function using parallel reads, parallel processing, and prefetch.
• D.Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

Comment: *

Name: *

Email: *

Verification: *

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

• A.Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
• B.Address data leakage by removing features highly correlated with the target value.
• C.Address data leakage by applying nested cross-validation during model training.
• D.Address the model overfitting by using a less complex algorithm.

Comment: *

Name: *

Email: *

Verification: *

You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

• A.Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
• B.Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
• C.Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
• D.Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.

Comment: *

Name: *

Email: *

Verification: *

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

• A.Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
• B.Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
• C.Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
• D.Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.

Comment: *

Name: *

Email: *

Verification: *