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NEW QUESTION 36
The displayed graph is from a forecasting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
- A. The model predicts both the trend and the seasonality well
- B. The model predicts the seasonality well, but not the trend.
- C. The model does not predict the trend or the seasonality well.
- D. The model predicts the trend well, but not the seasonality.
Answer: C
NEW QUESTION 37
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- C. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
- D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
Answer: B
NEW QUESTION 38
Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?
- A. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
- B. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
- C. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.
- D. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
Answer: A
NEW QUESTION 39
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
- A. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
- B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
- C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
- D. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
Answer: B
NEW QUESTION 40
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?
- A. Measure the effect of each feature as the weight of the feature multiplied by the feature value.
- B. Configure integrated gradients explanations on Vertex Explainable AI.
- C. Configure sampled Shapley explanations on Vertex Explainable AI.
- D. Train local surrogate models to explain individual predictions.
Answer: D
NEW QUESTION 41
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