Question No : 1
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 hyperparameter optimization (HPO)
• B.Distributing the training across multiple CPUs
• C.Using a variable learning late
• D.Training the model on multiple epochs

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Question No : 2
An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO) .
What experiment config fields configure this behavior?

• A.profiling: enabled: false
• B.hyperparameters; optimizer:none
• C.searcher: name: single
• D.resources: slots_per_trial: 1

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Question No : 3
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs .
What should you recommend?

• A.Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
• B.Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
• C.Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
• D.Establishing multiple compute resource pools on the cluster, one tor servers or each type

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Question No : 4
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.Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
• B.Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
• C.Double-checking that the checkpoint storage location is operating under 90% of total capacity.
• D.Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.

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Question No : 5
What are the mechanics of now a model trains?

• A.Decides which algorithm can best meet the use case for the application in question
• B.Adjusts the model's parameter weights such that the model can Better perform its tasks
• C.Tests how accurately the model performs on a wide array of real world data
• D.Detects Data drift of content drift that might compromise the ML model's performance

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Question No : 6
You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster .
What software must be installed on the machine before you run that command?

• A.Kubernetes
• B.PyTorch
• C.Terralorm
• D.Docker

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Question No : 7
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?

• A.Trial I is allowed to finish. Then Trial 3 is scheduled.
• B.Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
• C.Trial 1 is allowed to finish. Then Trial 2 is scheduled.
• D.Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

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Question No : 8
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
A. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
B. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
D. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.

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Answer:D