You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
A. Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
B. Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
C. Use Vertex AI's prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
D. Use Vertex AI's prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
正解:B
解説: (Pass4Test メンバーにのみ表示されます)
質問 2:
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
A. Create a Managed Instance Group with autoscaling
B. Use Vertex Al Platform for distributed training
C. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
D. Create a cluster on Dataproc for training
正解:B
解説: (Pass4Test メンバーにのみ表示されます)
質問 3:
You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
A. Use BigQuerys scheduling service to run the model retraining query periodically.
B. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
C. Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.
D. Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.
正解:C
解説: (Pass4Test メンバーにのみ表示されます)
質問 4:
You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)
A. 500*256+256*128+128*2 = 161024
B. 500*256*0 25+256*128*0 25+128*2 = 40448
C. 501*256+257*128+2 = 161154
D. 501*256+257*128+128*2=161408
正解:A
解説: (Pass4Test メンバーにのみ表示されます)
質問 5:
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
A. Create an object detection model that can localize the rust spots.
B. Develop a template matching algorithm using traditional computer vision libraries.
C. Develop an image classification ML model to predict the presence of the disease.
D. Develop an image segmentation ML model to locate the boundaries of the rust spots.
正解:D
解説: (Pass4Test メンバーにのみ表示されます)
吉田** -
問題や擬似問題集と回答などもあり、Professional-Machine-Learning-Engineer1冊で試験に対応できる良い本だと思います。