HP HPE2-N69 Exam Questions Instant Access

Question 1

What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?

AExperiment tracking

BModel Inferencing

CDistributed training

DPremium dedicated support

Answer : C

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.

Question 2

A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

AThe team wants to avoid wasting resources on training models with poorly selected hyperparameters.

BThe team wants to spend less time on creating the code tor models and more time training models.

CThe team wants to avoid training models to the point where they perform less well on new data.

DThe team wants to spend less time figuring out which CPUs are available for training models.

Answer : C

Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.

Question 3

Where does TensorFlow fit in the ML/DL Lifecycle?

Ait helps engineers use a language like Python to code and trail DL models.

Bit provides pipelines to manage the complete lifecycle.

CIt is primarily used to transport trained models to a deployment environment.

DIt adds system and GPU monitoring to the training process.

Answer : B

TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data ingestion to model training, evaluation, and deployment. It helps engineers use a language like Python to code and train DL models, and it also adds system and GPU monitoring to the training process. Additionally, it can be used to transport trained models to a deployment environment.

Question 4

You are helping a customer start to implement hyper parameter optimization (HPO) with HPE Machine learning Development Environment. An ML engineer is putting together an experiment config file with the desired Adaptive A5HA settings. The engineer asks you questions, such as how many trials will be trained on the max length and what the min length for all trials will be.

What should you explain?

AThe engineer should run the 'det preview-search' command, referencing the experiment config.

BThe engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.

CThe engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.

DThe engineer should run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.

Answer : B

The engineer should specify the number of trials to train on the max length and the minimum length for all trials in the experiment config file. For example, if the engineer wants to run 10 trials with a max length of 10, the config file should look something like this:

{

'mode': 'A5HA',

'max_trials': 10,

'max_length': 10,

'min_length': 1,

'divisor': 2,

'max_runs': 1

}

Once the config file is complete, the engineer should upload it to the HPE Machine Learning Development Environment WebUI and view the graph of the experiment plan. This will allow the engineer to see how the Adaptive A5HA settings will affect the experiment. After that, the engineer can run the experiment and assess the results.

Question 5

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 6

What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?

AThey run validation and checkpoint workloads.

BThey run training workloads that do not require GPUs.

CThey host management software such as the conductor and HPCM.

DThey run non-distributed training workloads.

Answer : C

HPE ProLiant DL325 servers play an important role in the HPE Machine Learning Development System. They are used to host the management software such as the Conductor and HPCM, and they also run non-distributed training workloads that do not require GPUs. They can also be used to run validation and checkpoint workloads.

Question 7

A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

ASet the weight to a higher than default value.

BSet the weight to a lower than default value.

CSet the priority to a lower than default value.

DSet the priority to a higher than default value.

Answer : A

Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.

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