[2024] New HPE2-N69 exam Free Sample Questions to Practice
Cover Real HPE2-N69 Exam Questions Make Sure You 100% Pass
HP HPE2-N69 certification exam is a specialized certification that confirms one's ability to use the HPE Cray AI Development Environment. Using HPE Cray AI Development Environment certification is designed for professionals who want to demonstrate their knowledge and skills in developing AI models and applications using this environment. The HPE Cray AI Development Environment is a high-performance computing platform that provides a comprehensive set of tools for developing AI applications in various industries.
NEW QUESTION # 14
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?
- A. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- B. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- C. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
Answer: B
NEW QUESTION # 15
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: A
NEW QUESTION # 16
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. K-12education
- C. Manufacturing
- D. Retail
Answer: C
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 # 17
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. The requirement that the ML team must wait for the IT team to initiate each new training process
- C. A lack of understanding of the DL model architecture by the NL engineering team
- D. The complexity of adjusting model code to distribute the training process across multiple GPUs
Answer: C
NEW QUESTION # 18
You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?
- A. How much time do you spend managing the ML infrastructure?
- B. How long does it currently take for a DL training to run the backward pass?
- C. How much do you understand about building ML and DL models?
- D. What frustrations do you have with existing ML deployment and differencing solutions?
Answer: D
Explanation:
A good discovery question to start a conversation about HPE Machine Learning Development Environment with an IT contact at a customer would be: "What frustrations do you have with existing ML deployment and differencing solutions?" By understanding the customer's current challenges and frustrations, you can better determine how HPE's ML Development Environment could help to address those needs.
NEW QUESTION # 19
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?
- A. The engineer should run the "det preview-search" command, referencing the experiment config.
- B. The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.
- C. 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.
- D. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
Answer: B
Explanation:
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.
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 I is allowed to finish. Then Trial 3 is scheduled.
- B. Trial 1 is allowed to finish. Then Trial 2 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 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
Answer: D
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 # 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 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 trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- D. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
Answer: D
NEW QUESTION # 22
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It ensures experiment metadata is stored.
- B. It uploads model checkpoints.
- C. it downloads datasets for training.
- D. It validates trained models.
Answer: B
NEW QUESTION # 23
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
- B. The team wants to avoid training models to the point where they perform less well on new data.
- C. The team wants to spend less time on creating the code tor models and more time training models.
- D. The team wants to spend less time figuring out which CPUs are available for training models.
Answer: D
NEW QUESTION # 24
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 # 25
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?
- A. Validation
- B. CPU-only Jupyter Notebook
- C. Training
- D. GPU Jupyter Notebook
Answer: B
NEW QUESTION # 26
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 # 27
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. it helps engineers use a language like Python to code and trail DL models.
- B. It is primarily used to transport trained models to a deployment environment.
- C. It adds system and GPU monitoring to the training process.
- D. it provides pipelines to manage the complete lifecycle.
Answer: A
NEW QUESTION # 28
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Equally distributing utilization across multiple agents
- B. Ensuring that the highest priority experiments obtain access to more resources
- C. Ensuring that all experiments receive their fair share of resources
- D. Minimizing costs in a cloud environment
Answer: D
NEW QUESTION # 29
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run training workloads that do not require GPUs.
- B. They run validation and checkpoint workloads.
- C. They run non-distributed training workloads.
- D. They host management software such as the conductor and HPCM.
Answer: D
NEW QUESTION # 30
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
- B. The team wants to avoid training models to the point where they perform less well on new data.
- C. The team wants to spend less time figuring out which CPUs are available for training models.
- D. The team wants to spend less time on creating the code tor models and more time training models.
Answer: B
Explanation:
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.
NEW QUESTION # 31
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. Distributing the training across multiple CPUs
- B. Training the model on multiple epochs
- C. Using hyperparameter optimization (HPO)
- D. Using a variable learning late
Answer: B
NEW QUESTION # 32
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 trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- D. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
Answer: D
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 # 33
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. The requirement that the ML team must wait for the IT team to initiate each new training process
- C. The complexity of adjusting model code to distribute the training process across multiple GPUs
- D. A lack of understanding of the DL model architecture by the NL engineering team
Answer: C
Explanation:
The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.
NEW QUESTION # 34
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