[Oct 23, 2023] HPE2-N69 Test Engine files, HPE2-N69 Dumps PDF
Latest HP HPE2-N69 PDF and Dumps (2023) Free Exam Questions Answers
NEW QUESTION # 11
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 understanding of the DL model architecture by the NL engineering team
- B. The requirement that the ML team must wait for the IT team to initiate each new training process
- C. A lack of adequate power and cooling for the GPU-enabled servers
- D. The complexity of adjusting model code to distribute the training process across multiple GPUs
Answer: A
NEW QUESTION # 12
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Pipeline-based data management
- B. Distributed training
- C. Automated user provisioning
- D. Automated hyperparameter optimization (HPO)
Answer: D
Explanation:
One of the main benefits of HPE Machine Learning Development Environment is its ability to automate the process of hyperparameter optimization (HPO). HPO is a process of automatically tuning the hyperparameters of a model during training, which can greatly improve a model's performance. HPE ML DE provides automated HPO, making the process of tuning and optimizing the model much easier and more efficient.
NEW QUESTION # 13
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. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- D. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
Answer: A
NEW QUESTION # 14
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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
- C. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- D. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
Answer: C
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 # 15
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?
- A. Trial I is allowed to finish. Then Trial 3 is scheduled.
- B. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- C. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- D. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
Answer: B
Explanation:
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.
NEW QUESTION # 16
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. Using a variable learning late
- B. Training the model on multiple epochs
- C. Distributing the training across multiple CPUs
- D. Using hyperparameter optimization (HPO)
Answer: B
NEW QUESTION # 17
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?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
The valid setting for "resourcespertrial" for the 10 agents in "my-compute-poor" with 8 GPUs each would be 20, as this would be the total number of GPUs available across all 10 agents. This setting would allow the experiment config to run on multiple GPUs at once.
NEW QUESTION # 18
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. Ensuring that the highest priority experiments obtain access to more resources
- C. Equally distributing utilization across multiple agents
- D. Minimizing costs in a cloud environment
Answer: B
Explanation:
The best fit policy on an HPE Machine Learning Development Environment resource pool ensures that the highest priority experiments obtain access to more resources, while still ensuring that all experiments receive their fair share. This allows you to make the most of your resources and prioritize the experiments that are most important to you.
NEW QUESTION # 19
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- B. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- C. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- D. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
Answer: B
Explanation:
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.
NEW QUESTION # 20
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?
- A. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- B. Trial I is allowed to finish. Then Trial 3 is scheduled.
- C. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- D. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
Answer: B
NEW QUESTION # 21
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. K-12education
- B. Retail
- C. Hospitality
- D. Manufacturing
Answer: D
Explanation:
One key target vertical for HPE Machine Learning Development solutions is Manufacturing. Manufacturing businesses are using machine learning to automate processes, reduce costs, and improve safety and quality control. HPE ML solutions provide the tools and technologies to help manufacturers develop and deploy ML models in their production environments, enabling them to optimize and automate their operations.
NEW QUESTION # 22
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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
- C. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- D. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
Answer: C
NEW QUESTION # 23
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. The trial can better separate training and validation data.
- C. Setting up streaming is easier that setting up downloading.
- D. Streaming requires just one bucket, while downloading requires many.
Answer: B
NEW QUESTION # 24
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It validates trained models.
- B. It ensures experiment metadata is stored.
- C. It uploads model checkpoints.
- D. it downloads datasets for training.
Answer: C
NEW QUESTION # 25
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?
- A. It helps DL projects complete faster for a faster ROI.
- B. It helps companies deploy models and generate revenue.
- C. It automatically cleans up data to create better end results.
- D. It uses a centralized training architecture that is highly efficient.
Answer: A
Explanation:
HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.
NEW QUESTION # 26
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 not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
- 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. Out you could recommend an educational HPE Pointnext ASPS workshop.
- D. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
Answer: A
NEW QUESTION # 27
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