HPE2-N69別格な問題集をダウンロードして無料で最新の（HPE2-N69テスト問題集をゲット2023年12月07日)
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質問 # 15
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Premium dedicated support
- C. Experiment tracking
- D. Model Inferencing
正解：A
解説：
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.
質問 # 16
Refer to the exhibit.
You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what "validation loss' means. What should you respond?
- A. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- B. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- C. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
- D. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
正解：D
質問 # 17
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Premium dedicated support
- B. Experiment tracking
- C. Distributed training
- D. Model Inferencing
正解：B
質問 # 18
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- B. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
- C. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
- D. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
正解：B
解説：
Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.
質問 # 19
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. What frustrations do you have with existing ML deployment and differencing solutions?
- C. How much do you understand about building ML and DL models?
- D. How long does it currently take for a DL training to run the backward pass?
正解：B
解説：
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.
質問 # 20
A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?
- A. Set the priority to a higher than default value.
- B. Set the weight to a lower than default value.
- C. Set the weight to a higher than default value.
- D. Set the priority to a lower than default value.
正解：C
質問 # 21
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 a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.
- 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 the "det preview-search" command, referencing the experiment config.
正解：A
質問 # 22
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
正解：A
質問 # 23
What are the mechanics of now a model trains?
- A. Detects Data drift of content drift that might compromise the ML model's performance
- B. Tests how accurately the model performs on a wide array of real world data
- C. Decides which algorithm can best meet the use case for the application in question
- D. Adjusts the model's parameter weights such that the model can Better perform its tasks
正解：D
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 # 24
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- B. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- D. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
正解：A
質問 # 25
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. it downloads datasets for training.
- B. It ensures experiment metadata is stored.
- C. It uploads model checkpoints.
- D. It validates trained models.
正解：B
解説：
The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.
質問 # 26
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. Streaming requires just one bucket, while downloading requires many.
- B. Setting up streaming is easier that setting up downloading.
- C. The trial can more quickly start up and begin training the model.
- D. The trial can better separate training and validation data.
正解：D
質問 # 27
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Manufacturing
- B. K-12education
- C. Hospitality
- D. Retail
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 # 28
You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?
- A. The number of agent GPUs
- B. The number of servers in the cluster
- C. The number of processor cores on agents
- D. The number of processor cores on all servers in the cluster
正解：A
質問 # 29
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
- A. Data Center Bridging (OCB)-enabled Ethernet
- B. InfiniBand
- C. Slingshot
- D. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
正解：A
質問 # 30
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- B. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- D. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
正解：A
解説：
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.
質問 # 31
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 long does it currently take for a DL training to run the backward pass?
- B. How much time do you spend managing the ML infrastructure?
- C. What frustrations do you have with existing ML deployment and differencing solutions?
- D. How much do you understand about building ML and DL models?
正解：A
質問 # 32
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. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- B. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- C. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- D. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
正解：D
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 # 33
What are the mechanics of now a model trains?
- A. Detects Data drift of content drift that might compromise the ML model's performance
- B. Tests how accurately the model performs on a wide array of real world data
- C. Decides which algorithm can best meet the use case for the application in question
- D. Adjusts the model's parameter weights such that the model can Better perform its tasks
正解：C
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 can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
- C. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- D. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
正解：A
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
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. It is primarily used to transport trained models to a deployment environment.
- B. it helps engineers use a language like Python to code and trail DL models.
- C. It adds system and GPU monitoring to the training process.
- D. it provides pipelines to manage the complete lifecycle.
正解：D
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 # 37
What is the role of a hidden layer in an artificial neural network (ANN)?
- A. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
- B. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
- C. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
- D. It is responsible for passively reformatting data for use in the ANN.
正解：B
解説：
A hidden layer in an artificial neural network (ANN) is responsible for receiving and weighing inputs from the preceding layer and producing outputs for the next layer. It is also responsible for reformatting data for use in the ANN and helps to optimize the ANN during the backward pass.
質問 # 38
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 servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- C. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- D. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
正解：A
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 # 39
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