Professional-Machine-Learning-Engineer 試験問題を無料オンラインアクセス

You work for an organization that operates a streaming music service. You have a custom production model that is serving a " next song " recommendation based on a user's recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

• A.Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user ' s selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production.
• B.Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.
• C.Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.
• D.Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model.

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

• A.
• B.
• C.
• D.

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.501*256+257*128+128*2=161408
• B.501*256+257*128+2 = 161154
• C.500*256+256*128+128*2 = 161024
• D.500*256*0 25+256*128*0 25+128*2 = 40448

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

• A.Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
• B.Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
• C.Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
• D.Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.

You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?

• A.Vertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata
• B.Dataplex. Vertex Al Experiments, and Vertex Al ML Metadata
• C.Dataplex. Vertex Al Feature Store and Vertex Al TensorBoard
• D.Vertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments