Latest [Jan 14, 2024] HP HPE2-N69 Real Exam Dumps PDF
HPE2-N69 Practice Test Questions Updated 42 Questions
NEW QUESTION # 24
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 do you understand about building ML and DL models?
- B. What frustrations do you have with existing ML deployment and differencing solutions?
- C. How much time do you spend managing the ML infrastructure?
- D. How long does it currently take for a DL training to run the backward pass?
Answer: B
Explanation:
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.
NEW QUESTION # 25
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
Answer: D
NEW QUESTION # 26
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 run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.
- 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 access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.
Answer: B
NEW QUESTION # 27
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. Detects Data drift of content drift that might compromise the ML model's performance
- C. Tests how accurately the model performs on a wide array of real world data
- D. Decides which algorithm can best meet the use case for the application in question
Answer: D
NEW QUESTION # 28
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that all experiments receive their fair share of resources
- B. Ensuring that the highest priority experiments obtain access to more resources
- C. Equally distributing utilization across multiple agents
- D. Minimizing costs in a cloud environment
Answer: D
NEW QUESTION # 29
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 servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- B. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- C. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- D. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
Answer: B
Explanation:
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.
NEW QUESTION # 30
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
Answer: A
NEW QUESTION # 31
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 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 conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
Answer: D
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 # 32
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 is metadata that indicates how many updates were lost between the conductor and agents.
- B. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
- C. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- 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: C
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 # 33
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 loss is metadata that indicates how many updates were lost between the conductor and agents.
- C. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- D. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
Answer: D
NEW QUESTION # 34
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 not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
- 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, but you could recommend open-source Determined Al.
- D. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
Answer: B
Explanation:
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion. With the customer's dedicated IT staff, the customer is ready to deploy an on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment is a comprehensive solution that provides the tools and technologies required to develop, manage, and deploy ML models. It includes a distributed training framework, an orchestration layer, a powerful development environment, and an integrated MLOps platform. With this solution, the customer can expand their ML/DL projects and scale up their team.
NEW QUESTION # 35
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. The trial can better separate training and validation data.
- B. The trial can more quickly start up and begin training the model.
- C. Streaming requires just one bucket, while downloading requires many.
- D. Setting up streaming is easier that setting up downloading.
Answer: A
NEW QUESTION # 36
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. Data Center Bridging (OCB)-enabled Ethernet
- C. Slingshot
- D. InfiniBand
Answer: B
NEW QUESTION # 37
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. Slingshot
- C. Data Center Bridging (OCB)-enabled Ethernet
- D. InfiniBand
Answer: A
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 # 38
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)
Answer: B
NEW QUESTION # 39
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.
Answer: D
NEW QUESTION # 40
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. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
- C. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
- D. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
Answer: D
NEW QUESTION # 41
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 spend less time on creating the code tor models and more time training models.
- C. The team wants to avoid training models to the point where they perform less well on new data.
- D. The team wants to spend less time figuring out which CPUs are available for training models.
Answer: C
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 # 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. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
- B. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
- C. Use the "det experiment download -top-n I" command, referencing 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: B
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 # 43
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