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NEW QUESTION 43
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
Answer: B
NEW QUESTION 44
What are the mechanics of now a model trains?
Answer: C
NEW QUESTION 45
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
Answer: A
NEW QUESTION 46
An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:
* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50
* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I
What happens?
Answer: A
Explanation:
Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.
NEW QUESTION 47
ML engineers are defining a convolutional neural network (CNN) model bur they are not sure how many filters to use in each convolutional layer. What can help them address this concern?
Answer: C
Explanation:
Hyperparameter optimization is a process of tuning the hyperparameters of a machine learning model, such as the number of filters in a convolutional neural network (CNN) model, to determine the best combination of hyperparameters that will result in the best model performance. HPO techniques are used to automatically find the optimal hyperparameter values, which can greatly increase the accuracy and performance of the model.
NEW QUESTION 48
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