[2023年更新]合格できるHP HPE2-N69プレミアム資料テストエンジンPDFの無料問題集お試しセット
2023年最新のリアルHPE2-N69問題集テストエンジン試験問題はここにある
HP HPE2-N69 認定試験の出題範囲：
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質問 23
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run validation and checkpoint workloads.
- B. They run non-distributed training workloads.
- C. They run training workloads that do not require GPUs.
- D. They host management software such as the conductor and HPCM.
正解: D
質問 24
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. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- C. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- D. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
正解: A
解説:
The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.
質問 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 automatically cleans up data to create better end results.
- C. It helps companies deploy models and generate revenue.
- D. It uses a centralized training architecture that is highly efficient.
正解: A
質問 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 a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
- B. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
- 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 not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
正解: D
質問 27
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?
- A. Re-enter the command with a period (.) at the end.
- B. Make sure that you have already logged into the cluster with the "det login'' command.
- C. Re-enter the command with "-m" in which is the code filename.
- D. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
正解: D
質問 28
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Automated user provisioning
- B. Pipeline-based data management
- C. Distributed training
- D. Automated hyperparameter optimization (HPO)
正解: D
解説:
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.
質問 29
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It uploads model checkpoints.
- B. It validates trained models.
- C. it downloads datasets for training.
- D. It ensures experiment metadata is stored.
正解: A
質問 30
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. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- B. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- C. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
正解: C
解説:
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.
質問 31
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
- A. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
- B. InfiniBand
- C. Slingshot
- D. Data Center Bridging (OCB)-enabled Ethernet
正解: A
解説:
HPE Machine Learning Development System uses Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE) for high-speed, agent-to-agent communications. This technology allows data to be transferred directly between agents without the need for copying, which results in improved performance and reduced latency.
質問 32
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Automated hyperparameter optimization (HPO)
- B. Automated user provisioning
- C. Distributed training
- D. Pipeline-based data management
正解: D
質問 33
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 upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
- D. 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.
正解: B
解説:
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.
質問 34
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. searcher: name: single
- B. profiling: enabled: false
- C. hyperparameters; optimizer:none
- D. resources: slots_per_trial: 1
正解: C
解説:
To train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO), you need to set the "optimizer" field to "none" in the hyperparameters section of the experiment config. This will instruct the ML engine to not use any hyperparameter optimization when training the model.
質問 35
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. Windows 10 or above
- B. Windows Server 2016 or above
- C. HP-UX v11i
- D. Red Hat 7-based Linux
正解: D
解説:
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.
質問 36
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. searcher: name: single
- B. profiling: enabled: false
- C. resources: slots_per_trial: 1
- D. hyperparameters; optimizer:none
正解: C
質問 37
A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?
- A. Complex and time-consuming hyperparameter optimization (HPO)
- B. Time-consuming data collection
- C. Complex and time-consuming data cleansing process
- D. Complex model deployment processes
正解: C
質問 38
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 automatically cleans up data to create better end results.
- C. It helps companies deploy models and generate revenue.
- D. It uses a centralized training architecture that is highly efficient.
正解: A
解説:
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.
質問 39
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. it provides pipelines to manage the complete lifecycle.
- B. it helps engineers use a language like Python to code and trail DL models.
- C. It is primarily used to transport trained models to a deployment environment.
- D. It adds system and GPU monitoring to the training process.
正解: B
質問 40
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. PyTorch
- B. Terralorm
- C. Docker
- D. Kubernetes
正解: D
質問 41
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 a compute pool
- D. Any of the agent's in an aux pool
正解: B
解説:
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.
質問 42
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 trained through multiple training processes implemented by different team members
- D. Models defined with Apache Spark rather than MapReduce
正解: B
質問 43
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Manufacturing
- B. Retail
- C. K-12education
- D. Hospitality
正解: A
解説:
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.
質問 44
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
- B. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
- C. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- D. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
正解: D
質問 45
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. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
- C. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- D. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
正解: D
質問 46
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 "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- C. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
- D. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
正解: B
解説:
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.
質問 47
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最新オフィシャル資料はHPE2-N69認証されたHPE2-N69問題集PDF：https://jp.fast2test.com/HPE2-N69-premium-file.html
最新推薦するHPE2-N69問題集はHPE Product Certified - AI and Machine Learning認証された：https://drive.google.com/open?id=1qpwhFc-t98NtA_Th-zpKv8kjDB6J0XPW