HP HPE2-N69 Exam Questions Instant Access

Question 1

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?

AAdjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.

BMonitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.

CDouble-checking that the checkpoint storage location is operating under 90% of total capacity.

DAdjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.

Answer : A

The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.

Question 2

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?

ATrial I is allowed to finish. Then Trial 3 is scheduled.

BTrial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.

CTrial 1 is allowed to finish. Then Trial 2 is scheduled.

DTrial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

Answer : D

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.

Question 3

Refer to the exhibit.

You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what "validation loss' means. What should you respond?

AValidation refers to testing how well the current model performs on new data; file lower the loss the better the performance.

BValidation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.

CValidation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.

DValidation loss is metadata that indicates how many updates were lost between the conductor and agents.

Answer : A

Validation loss is a metric used to measure how well the model is performing on unseen data. It is calculated by taking the difference between the predicted values and the actual values. The lower the validation loss, the better the model's performance on new data.

Question 4

What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

Ait downloads datasets for training.

BIt uploads model checkpoints.

CIt validates trained models.

DIt ensures experiment metadata is stored.

Answer : D

The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.

Question 5

At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?

AAny of the agent's in a compute pool

BA virtual one assigned to the cluster

CThe conductor's

DAny of the agent's in an aux pool

Answer : C

The WebUI for an HPE Machine Learning Development cluster can be accessed at the FQDN or IP address of the conductor. The conductor is responsible for managing the cluster and providing access to the WebUI.

Question 6

You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?

AHow long does it currently take for a DL training to run the backward pass?

BHow much do you understand about building ML and DL models?

CHow much time do you spend managing the ML infrastructure?

DWhat frustrations do you have with existing ML deployment and differencing solutions?

Answer : D

A good discovery question to start a conversation about HPE Machine Learning Development Environment with an IT contact at a customer would be: 'What frustrations do you have with existing ML deployment and differencing solutions?' By understanding the customer's current challenges and frustrations, you can better determine how HPE's ML Development Environment could help to address those needs.

Question 7

Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

AAdaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.

BASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.

CAdaptive ASHA can train more trials in certain amount of time, as compared to ASHA.

DAdaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.

Answer : B

Adaptive ASHA is an enhanced version of ASHA that uses a reinforcement learning approach to select hyperparameter configurations. This allows Adaptive ASHA to select higher-performing configs and clone those configurations, allowing for better performance than ASHA.

HP Using HPE AI and Machine Learning HPE2-N69 Exam Questions