2023年06月実際に出るHPE2-N69試験問題集には正確で更新された問題
HPE2-N69試験問題集でPDF問題とテストエンジン
質問 # 16
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. resources: slots_per_trial: 1
- C. hyperparameters; optimizer:none
- D. profiling: enabled: false
正解：B
質問 # 17
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 fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
正解：B
解説：
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.
質問 # 18
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. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- B. Make sure that you have already logged into the cluster with the "det login'' command.
- C. Re-enter the command with a period (.) at the end.
- D. Re-enter the command with "-m" in which is the code filename.
正解：A
解説：
Make sure that the myfile.yaml tile includes code for a PyTorchTrial or TFKerasTrial class. When creating an experiment with the det experiment create command, you need to specify the model_def parameter to provide the code for the PyTorchTrial or TFKerasTrial class. This code should be specified in the myfile.yaml file, so make sure that the myfile.yaml file includes the code for the model you want to use.
質問 # 19
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that the highest priority experiments obtain access to more resources
- B. Ensuring that all experiments receive their fair share of resources
- C. Equally distributing utilization across multiple agents
- D. Minimizing costs in a cloud environment
正解：A
解説：
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.
質問 # 20
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run non-distributed training workloads.
- B. They run training workloads that do not require GPUs.
- C. They run validation and checkpoint workloads.
- D. They host management software such as the conductor and HPCM.
正解：D
解説：
HPE ProLiant DL325 servers play an important role in the HPE Machine Learning Development System. They are used to host the management software such as the Conductor and HPCM, and they also run non-distributed training workloads that do not require GPUs. They can also be used to run validation and checkpoint workloads.
質問 # 21
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 Server 2016 or above
- B. Windows 10 or above
- C. Red Hat 7-based Linux
- D. HP-UX v11i
正解：D
質問 # 22
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 hyperparameter optimization (HPO)
- B. Training the model on multiple epochs
- C. Distributing the training across multiple CPUs
- D. Using a variable learning late
正解：A
解説：
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.
質問 # 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. Setting up streaming is easier that setting up downloading.
- B. The trial can more quickly start up and begin training the model.
- C. Streaming requires just one bucket, while downloading requires many.
- D. The trial can better separate training and validation data.
正解：D
質問 # 24
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Retail
- B. K-12education
- C. Hospitality
- D. Manufacturing
正解：D
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 # 25
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. It adds system and GPU monitoring to the training process.
- B. It is primarily used to transport trained models to a deployment environment.
- C. it provides pipelines to manage the complete lifecycle.
- D. it helps engineers use a language like Python to code and trail DL models.
正解：C
解説：
TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data ingestion to model training, evaluation, and deployment. It helps engineers use a language like Python to code and train DL models, and it also adds system and GPU monitoring to the training process. Additionally, it can be used to transport trained models to a deployment environment.
質問 # 26
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. GPU Jupyter Notebook
- B. Training
- C. Validation
- D. CPU-only Jupyter Notebook
正解：D
質問 # 27
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 trained through multiple training processes implemented by different team members
- C. Models defined with Apache Spark rather than MapReduce
- D. Models that are trained through unsupervised, rather than supervised, training
正解：D
質問 # 28
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.
正解：A
質問 # 29
What are the mechanics of now a model trains?
- A. Tests how accurately the model performs on a wide array of real world data
- B. Adjusts the model's parameter weights such that the model can Better perform its tasks
- C. Detects Data drift of content drift that might compromise the ML model's performance
- D. Decides which algorithm can best meet the use case for the application in question
正解：B
解説：
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.
質問 # 30
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
- A. InfiniBand
- B. Data Center Bridging (OCB)-enabled Ethernet
- C. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
- D. Slingshot
正解：C
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 # 31
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Retail
- B. K-12education
- C. Hospitality
- D. Manufacturing
正解：D
質問 # 32
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 two HPE Machine Learning Development Environment clusters, one tor each server type
- 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 servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
正解：C
質問 # 33
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
- A. InfiniBand
- B. Data Center Bridging (OCB)-enabled Ethernet
- C. Slingshot
- D. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
正解：B
質問 # 34
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 automatically mirrors the training process on redundant agents, which take over If an issue occurs.
- B. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- C. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
- D. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
正解：C
解説：
The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.
質問 # 35
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. GPU Jupyter Notebook
- B. Training
- C. Validation
- D. CPU-only Jupyter Notebook
正解：D
解説：
Pool 1 has two agents, each with 100 aux containers, and a total of 0 slots. This means that the cluster is configured to run CPU-only workloads, such as running a CPU-only Jupyter Notebook. Training, GPU Jupyter Notebook, and validation workloads cannot be run on this cluster due to the lack of GPU resources.
質問 # 36
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