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NEW QUESTION 45
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error. What should you do?
- A. Send the request again with a smaller batch of instances.
- B. Apply for a quota increase for the number of prediction requests.
- C. Use base64 to encode your data before using it for prediction.
- D. Use batch prediction mode instead of online mode.
Answer: C
NEW QUESTION 46
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. Use Al Platform for distributed training
- B. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
- C. Create a cluster on Dataproc for training
- D. Create a Managed Instance Group with autoscaling
Answer: B
NEW QUESTION 47
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?
- A. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
- B. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
Answer: B
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION 48
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?
- A. Migrate your model to TensorFlow, and train it using Vertex AI Training.
- B. Train your model in a distributed mode using multiple Compute Engine VMs.
- C. Train your model using Vertex AI Training with GPUs.
- D. Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.
Answer: D
NEW QUESTION 49
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