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HP Using HPE Cray AI Development Environment Sample Questions (Q34-Q39):
NEW QUESTION # 34
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid training models to the point where they perform less well on new data.
- B. The team wants to spend less time figuring out which CPUs are available for training models.
- C. The team wants to spend less time on creating the code tor models and more time training models.
- D. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
NEW QUESTION # 35
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Premium dedicated support
- C. Model Inferencing
- D. Experiment tracking
The benefit of HPE Machine Learning Development Environment beyond open source Determined AI is Distributed Training. Distributed training allows multiple machines to train a single model in parallel, greatly increasing the speed and efficiency of the training process. HPE ML Development Environment provides tools and support for distributed training, allowing users to make the most of their resources and quickly train their models.
NEW QUESTION # 36
An ML engineer is running experiments on HPE Machine Learning Development Environment. The engineer notices all of the checkpoints for a trial except one disappear after the trial ends. The engineer wants to Keep more of these checkpoints. What can you recommend?
- A. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- B. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- C. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- D. Adjusting how many of the latest and best checkpoints are saved in the experiment config’s checkpoint storage settings.
NEW QUESTION # 37
What is the role of a hidden layer in an artificial neural network (ANN)?
- A. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
- B. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
- C. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
- D. It is responsible for passively reformatting data for use in the ANN.
A hidden layer in an artificial neural network (ANN) is responsible for receiving and weighing inputs from the preceding layer and producing outputs for the next layer. It is also responsible for reformatting data for use in the ANN and helps to optimize the ANN during the backward pass.
NEW QUESTION # 38
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?
- A. Using a variable learning late
- B. Using hyperparameter optimization (HPO)
- C. Training the model on multiple epochs
- D. Distributing the training across multiple CPUs
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 # 39
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