HPE2-N69 Dumps with Practice Exam Questions Answers
HPE2-N69 by HPE Product Certified - AI and Machine Learning Actual Free Exam Practice Test
NEW QUESTION # 24
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?
- A. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
- B. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
- C. The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
- D. The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
Answer: A
Explanation:
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion. With the customer's dedicated IT staff, the customer is ready to deploy an on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment is a comprehensive solution that provides the tools and technologies required to develop, manage, and deploy ML models. It includes a distributed training framework, an orchestration layer, a powerful development environment, and an integrated MLOps platform. With this solution, the customer can expand their ML/DL projects and scale up their team.
NEW QUESTION # 25
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 # 26
The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources. What can the engineer do in the experiment config file to help support this goal?
- A. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- B. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
- C. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- D. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
Answer: C
NEW QUESTION # 27
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. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- B. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- C. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
Answer: A
NEW QUESTION # 28
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 concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- C. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
- D. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
Answer: B
NEW QUESTION # 29
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. Docker
- B. Terralorm
- C. PyTorch
- D. Kubernetes
Answer: A
Explanation:
Before running the "del deploy" command to set up the cluster, you must first install Docker on the machine. Docker is a containerization platform that is used to run applications in an isolated environment. It is necessary to have Docker installed before running the "del deploy" command to set up the cluster for the open source Determined AI on a local machine.
NEW QUESTION # 30
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. A virtual one assigned to the cluster
- B. The conductor's
- C. Any of the agent's in an aux pool
- D. Any of the agent's in a compute pool
Answer: B
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 # 31
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. The complexity of adjusting model code to distribute the training process across multiple GPUs
- B. A lack of understanding of the DL model architecture by the NL engineering team
- C. A lack of adequate power and cooling for the GPU-enabled servers
- D. The requirement that the ML team must wait for the IT team to initiate each new training process
Answer: B
NEW QUESTION # 32
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
- A. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers
- B. Models that are trained through unsupervised, rather than supervised, training
- C. Models defined with Apache Spark rather than MapReduce
- D. Models trained through multiple training processes implemented by different team members
Answer: A
Explanation:
Models based on neural networks with interconnected layers of nodes, including multiple hidden layers. Deep learning (DL) is a type of machine learning (ML) that uses models based on neural networks with interconnected layers of nodes, including multiple hidden layers. This is what distinguishes it from other forms of ML, which typically use simpler models with fewer layers. The multiple layers of DL models enable them to learn complex patterns and features from the data, allowing for more accurate and powerful predictions.
NEW QUESTION # 33
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Premium dedicated support
- B. Model Inferencing
- C. Experiment tracking
- D. Distributed training
Answer: D
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 # 34
The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources. What can the engineer do in the experiment config file to help support this goal?
- A. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- B. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
- C. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- D. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
Answer: C
Explanation:
The ML engineer can set "maxconcurrenttrials" under "searcher" in the experiment config file to cap the number of trials run at once by this experiment. This will help ensure that the experiment does not take up too large a share of resources, allowing other experiments to also run concurrently.
NEW QUESTION # 35
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. Manufacturing
- C. Retail
- D. K-12education
Answer: B
NEW QUESTION # 36
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 more quickly start up and begin training the model.
- B. Streaming requires just one bucket, while downloading requires many.
- C. The trial can better separate training and validation data.
- D. Setting up streaming is easier that setting up downloading.
Answer: A
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 # 37
You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?
- A. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- B. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
- C. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
- D. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.
Answer: D
NEW QUESTION # 38
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. Training the model on multiple epochs
- B. Using a variable learning late
- C. Distributing the training across multiple CPUs
- D. Using hyperparameter optimization (HPO)
Answer: D
Explanation:
Hyperparameter optimization is a process of tuning the hyperparameters of a machine learning model, such as the number of filters in a convolutional neural network (CNN) model, to determine the best combination of hyperparameters that will result in the best model performance. HPO techniques are used to automatically find the optimal hyperparameter values, which can greatly increase the accuracy and performance of the model.
NEW QUESTION # 39
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