[2023] Use Valid New Free HPE2-N69 Exam Dumps & Answers
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NEW QUESTION 21
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. Training
- B. Validation
- C. CPU-only Jupyter Notebook
- D. GPU Jupyter Notebook
Answer: C
NEW QUESTION 22
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. Time-consuming data collection
- B. Complex and time-consuming hyperparameter optimization (HPO)
- C. Complex model deployment processes
- D. Complex and time-consuming data cleansing process
Answer: B
Explanation:
The HPE Machine Learning Development Environment specifically addresses Complex and time-consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.
NEW QUESTION 23
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 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- C. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
- 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 24
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.
Answer: B
Explanation:
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.
NEW QUESTION 25
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 continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- B. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
- C. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
- D. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.
Answer: B
Explanation:
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.
NEW QUESTION 26
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. Any of the agent's in a compute pool
- B. A virtual one assigned to the cluster
- C. Any of the agent's in an aux pool
- D. The conductor's
Answer: C
NEW QUESTION 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 "-m" in which is the code filename.
- 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 a period (.) at the end.
Answer: C
Explanation:
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.
NEW QUESTION 28
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. hyperparameters; optimizer:none
- B. profiling: enabled: false
- C. resources: slots_per_trial: 1
- D. searcher: name: single
Answer: C
NEW QUESTION 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. resources: slots_per_trial: 1
- B. profiling: enabled: false
- C. hyperparameters; optimizer:none
- D. searcher: name: single
Answer: C
Explanation:
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.
NEW QUESTION 30
What are the mechanics of now a model trains?
- A. Adjusts the model's parameter weights such that the model can Better perform its tasks
- B. Decides which algorithm can best meet the use case for the application in question
- C. Detects Data drift of content drift that might compromise the ML model's performance
- D. Tests how accurately the model performs on a wide array of real world data
Answer: A
Explanation:
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.
NEW QUESTION 31
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 loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
- B. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- C. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
- D. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
Answer: B
Explanation:
Validation loss is a metric used to measure how well the model is performing on unseen data. It is calculated by taking the difference between the predicted values and the actual values. The lower the validation loss, the better the model's performance on new data.
NEW QUESTION 32
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. Streaming requires just one bucket, while downloading requires many.
- C. The trial can better separate training and validation data.
- D. The trial can more quickly start up and begin training the model.
Answer: C
NEW QUESTION 33
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 validates trained models.
- D. It uploads model checkpoints.
Answer: B
Explanation:
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.
NEW QUESTION 34
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. Slingshot
- C. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
- D. InfiniBand
Answer: C
Explanation:
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.
NEW QUESTION 35
What are the mechanics of now a model trains?
- A. Adjusts the model's parameter weights such that the model can Better perform its tasks
- B. Decides which algorithm can best meet the use case for the application in question
- C. Detects Data drift of content drift that might compromise the ML model's performance
- D. Tests how accurately the model performs on a wide array of real world data
Answer: B
NEW QUESTION 36
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 a variable learning late
- B. Using hyperparameter optimization (HPO)
- C. Training the model on multiple epochs
- D. Distributing the training across multiple CPUs
Answer: B
Explanation:
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.
NEW QUESTION 37
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 conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- 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 concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
Answer: A
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 38
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: 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 39
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. K-12education
- B. Manufacturing
- C. Retail
- D. Hospitality
Answer: B
NEW QUESTION 40
Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment. What can you do?
- A. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
- B. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- C. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
- D. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
Answer: A
Explanation:
HPE Machine Learning Development Environment uses Amazon S3 to store checkpoints. To find the location of the best checkpoint created during an experiment, you need to look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID. This bucket will contain all of the checkpoints that were created during the experiment.
NEW QUESTION 41
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.
Answer: B
NEW QUESTION 42
Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment. What can you do?
- A. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- B. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
- C. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
- D. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
Answer: A
NEW QUESTION 43
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