無料HP HPE2-N69テスト練習問題試験問題集
試験準備には欠かさない！トップクラスのHP HPE2-N69試験最新版アプリ学習ガイドで練習
質問 # 24
What is the role of a hidden layer in an artificial neural network (ANN)?
- A. It is responsible for passively reformatting data for use in the ANN.
- B. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
- C. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
- D. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
正解：C
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 # 25
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 1 is allowed to finish. Then Trial 2 is scheduled.
- B. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- C. Trial I is allowed to finish. Then Trial 3 is scheduled.
- D. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
正解：D
解説：
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.
質問 # 26
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 # 27
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 a variable learning late
- D. Using hyperparameter optimization (HPO)
正解：B
質問 # 28
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 weight to a lower than default value.
- B. Set the weight to a higher than default value.
- C. Set the priority to a lower than default value.
- D. Set the priority to a higher than default value.
正解：B
解説：
Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.
質問 # 29
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. profiling: enabled: false
- B. resources: slots_per_trial: 1
- C. hyperparameters; optimizer:none
- D. searcher: name: single
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 # 30
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Manufacturing
- B. Hospitality
- C. K-12education
- D. Retail
正解：A
質問 # 31
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. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- C. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
正解：B
質問 # 32
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. profiling: enabled: false
- B. hyperparameters; optimizer:none
- C. searcher: name: single
- D. resources: slots_per_trial: 1
正解：D
質問 # 33
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. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- D. Re-enter the command with "-m" in which is the code filename.
正解：C
質問 # 34
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. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- B. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- C. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- D. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
正解：D
質問 # 35
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. What frustrations do you have with existing ML deployment and differencing solutions?
- B. How much time do you spend managing the ML infrastructure?
- C. How long does it currently take for a DL training to run the backward pass?
- D. How much do you understand about building ML and DL models?
正解：A
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 # 36
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. HP-UX v11i
- C. Windows Server 2016 or above
- 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 # 37
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. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- C. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
正解：B
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 # 38
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. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
- C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- D. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
正解：D
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 # 39
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. Kubernetes
- C. Terralorm
- D. Docker
正解：D
解説：
Before running the "del deploy" command to set up the cluster, you must first install Docker on the machine. Docker is a containerization platform that is used to run applications in an isolated environment. It is necessary to have Docker installed before running the "del deploy" command to set up the cluster for the open source Determined AI on a local machine.
質問 # 40
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HPのHPE2-N69試験は、HPE Cray AI開発環境を使用する専門知識を習得したい個人を対象としています。この試験は、HPE Cray AI開発環境で作業する専門家が、この分野におけるスキルと知識を検証したい場合に適しています。この試験に合格することで、候補者がHPE Cray AI開発環境を使用してAIベースのアプリケーションを設計、開発、展開するために必要なスキルを持っていることが証明されます。
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