Apr-2023 Latest Real4dumps HPE2-N69 Exam Dumps with PDF and Exam Engine Free Updated Today!
Following are some new HPE2-N69 Real Exam Questions!
NEW QUESTION 19
You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use "det deploy" to set up the cluster. What is one prerequisite?
- A. installing the NVIDIA Container Toolkit on your local machine
- B. Manually creating the AWS EC2 instance with a PostgreSQL database
- C. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
- D. Recording the name of a valid AWS EC2 keypair
Answer: D
Explanation:
In order to use the "det deploy" command to set up a cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS), you will need to have a valid AWS EC2 keypair. The keypair will authenticate your access to the cluster and allow you to securely access the cluster once it is set up.
NEW QUESTION 20
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
- A. Red Hat 7-based Linux
- B. Windows 10 or above
- C. Windows Server 2016 or above
- D. HP-UX v11i
Answer: A
Explanation:
The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.
NEW QUESTION 21
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment.
That GPU fails. What happens next?
- A. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
Answer: B
NEW QUESTION 22
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. Terralorm
- C. PyTorch
- D. Docker
Answer: A
NEW QUESTION 23
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. The conductor's
- B. Any of the agent's in an aux pool
- C. A virtual one assigned to the cluster
- D. Any of the agent's in a compute pool
Answer: A
Explanation:
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.
NEW QUESTION 24
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 tor "resources_per_trial?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
NEW QUESTION 25
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. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- B. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- C. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- D. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
Answer: C
Explanation:
By establishing multiple compute resource pools on the cluster, you can ensure that the correct servers are used for each experiment, depending on the number of GPUs required. This will help ensure that the experiments are run on the servers with the correct resources without having to manually assign each experiment to the appropriate server.
NEW QUESTION 26
What are the mechanics of now a model trains?
- A. Detects Data drift of content drift that might compromise the ML model's performance
- 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. Decides which algorithm can best meet the use case for the application in question
Answer: B
Explanation:
This is done by running the model through a training loop, where the model is fed data and the parameter weights are adjusted based on the results of the model's performance on the data. For example, if the model is a neural network, the weights of the connections between the neurons are adjusted based on the results of the model's performance on the data. This process is repeated until the model performs better on the data, at which point the model is considered trained.
NEW QUESTION 27
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run non-distributed training workloads.
- B. They host management software such as the conductor and HPCM.
- C. They run training workloads that do not require GPUs.
- D. They run validation and checkpoint workloads.
Answer: B
NEW QUESTION 28
You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?
- A. The number of servers in the cluster
- B. The number of processor cores on all servers in the cluster
- C. The number of processor cores on agents
- D. The number of agent GPUs
Answer: B
Explanation:
The license count for the HPE Machine Learning Development Environment solution would be based on the number of processor cores on all servers in the cluster. This includes all servers in the cluster, regardless of whether they are running agents or not. Each processor core in the cluster requires a license and these licenses can be purchased in packs of 2, 4, 8, and 16.
NEW QUESTION 29
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It validates trained models.
- B. It uploads model checkpoints.
- C. It ensures experiment metadata is stored.
- D. it downloads datasets for training.
Answer: B
NEW QUESTION 30
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that all experiments receive their fair share of resources
- B. Equally distributing utilization across multiple agents
- C. Ensuring that the highest priority experiments obtain access to more resources
- D. Minimizing costs in a cloud environment
Answer: D
NEW QUESTION 31
You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?
- A. The number of servers in the cluster
- B. The number of agent GPUs
- C. The number of processor cores on agents
- D. The number of processor cores on all servers in the cluster
Answer: B
NEW QUESTION 32
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 on creating the code tor models and more time training models.
- C. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
- D. The team wants to spend less time figuring out which CPUs are available for training models.
Answer: D
NEW QUESTION 33
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. hyperparameters; optimizer:none
- B. profiling: enabled: false
- C. resources: slots_per_trial: 1
- D. searcher: name: single
Answer: C
NEW QUESTION 34
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Pipeline-based data management
- C. Automated hyperparameter optimization (HPO)
- D. Automated user provisioning
Answer: B
NEW QUESTION 35
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?
- A. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- B. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- C. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
- D. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
Answer: A
Explanation:
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.
NEW QUESTION 36
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Premium dedicated support
- C. Experiment tracking
- D. Model Inferencing
Answer: A
Explanation:
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 37
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. A lack of adequate power and cooling for the GPU-enabled servers
- B. A lack of understanding of the DL model architecture by the NL engineering team
- C. The requirement that the ML team must wait for the IT team to initiate each new training process
- D. The complexity of adjusting model code to distribute the training process across multiple GPUs
Answer: B
NEW QUESTION 38
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Experiment tracking
- C. Premium dedicated support
- D. Model Inferencing
Answer: B
NEW QUESTION 39
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?
- A. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
Answer: B
Explanation:
If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.
NEW QUESTION 40
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?
- A. Set the priority to a lower than default value.
- B. Set the weight to a higher than default value.
- C. Set the priority to a higher than default value.
- D. Set the weight to a lower than default value.
Answer: B
Explanation:
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.
NEW QUESTION 41
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?
- A. The trial can better separate training and validation data.
- B. Setting up streaming is easier that setting up downloading.
- C. The trial can more quickly start up and begin training the model.
- D. Streaming requires just one bucket, while downloading requires many.
Answer: C
Explanation:
Streaming the data during a trial allows the data to be processed more quickly, as it does not need to be downloaded onto the cluster before training can begin. This means that the trial can start up faster and the model can begin training more quickly.
NEW QUESTION 42
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?
- A. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- B. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
- C. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
- D. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
Answer: C
NEW QUESTION 43
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