Pass HP HPE2-N69 exam questions – convert Test Engine to PDF [Q15-Q31]

Rate this post

Pass HP HPE2-N69 exam questions – convert Test Engine to PDF

Pass Your HPE2-N69 Exam Easily – Real HPE2-N69 Practice Dump Updated Nov 05, 2023

HPE Cray AI development environment is designed to help developers build and deploy AI solutions quickly and easily. It provides a comprehensive set of tools and technologies for building and training AI models, including support for deep learning frameworks such as TensorFlow and PyTorch. With HPE Cray AI development environment, developers can easily create, train, and deploy AI models on a wide range of hardware platforms, including CPUs, GPUs, and FPGAs.

NO.15 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?

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

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

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

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

NO.16 The 10 agents in “my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting for “resources_per_trial?

NO.17 What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.

Implementing HPO manually can be time-consuming and demand a great deal of expertise.

ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

NO.18 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?

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

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

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

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

NO.19 A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS). What is an advantage of streaming the data during a trial, as opposed to downloading the data?

Streaming requires just one bucket, while downloading requires many.

The trial can more quickly start up and begin training the model.

The trial can better separate training and validation data.

Setting up streaming is easier that setting up downloading.

NO.20 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?

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

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

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

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

NO.21 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?

Kubernetes

PyTorch

Terralorm

Docker

NO.22 You are in a directory on your machine with your experiment config file and your model code. You enter this command:
det experiment create myfile.yaml
You receive this error:
det experiment create: error: the following arguments are required: model_def What should you do?

Re-enter the command with “-m” in which is the code filename.

Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.

Re-enter the command with a period (.) at the end.

Make sure that you have already logged into the cluster with the “det login” command.

NO.23 What distinguishes deep learning (DL) from other forms of machine learning (ML)?

Models based on neural networks with interconnected layers of nodes, including multiple hidden layers

Models defined with Apache Spark rather than MapReduce

Models that are trained through unsupervised, rather than supervised, training

Models trained through multiple training processes implemented by different team members

NO.24 A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

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

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

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

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

NO.25 A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?

A lack of understanding of the DL model architecture by the NL engineering team

The complexity of adjusting model code to distribute the training process across multiple GPUs

A lack of adequate power and cooling for the GPU-enabled servers

The requirement that the ML team must wait for the IT team to initiate each new training process

NO.26 An HPE Machine Learning Development Environment cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?

Training

GPU Jupyter Notebook

Validation

CPU-only Jupyter Notebook

NO.27 What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?

Experiment tracking

Model Inferencing

Distributed training

Premium dedicated support

NO.28 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?

The engineer should run the “det preview-search” command, referencing the experiment config.

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

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

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

NO.29 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?

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

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

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

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

NO.30 What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

Ensuring that all experiments receive their fair share of resources

Minimizing costs in a cloud environment

Equally distributing utilization across multiple agents

Ensuring that the highest priority experiments obtain access to more resources

NO.31 You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?

The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.

The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.

The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.

The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.

HP HPE2-N69 certification exam is designed for individuals who want to validate their knowledge and skills in using HPE Cray AI development environment. Using HPE Cray AI Development Environment certification exam is intended for AI developers, data scientists, and IT professionals who work with HPE AI solutions. HPE2-N69 exam measures the candidates’ ability to design, configure, and manage HPE Cray AI development environment, including the HPE Cray Programming Environment (CPE), HPE Cray AI Library (CAL), and HPE Cray AI Runtime (CAR).

HPE2-N69 Real Exam Questions and Answers FREE: https://www.braindumpstudy.com/HPE2-N69_braindumps.html