Dec-2023 Realistic HPE2-N69 Accurate & Verified Answers As Experienced in the Actual Test!
Latest HP HPE2-N69 Practice Test Questions, Using HPE Cray AI Development Environment Exam Dumps
Hewlett Packard Enterprise (HPE), a renowned multinational technology product innovator, offers the cutting-edge certification program HPE2-N69: Using HPE Cray AI Development Environment exam. HPE2-N69 exam focuses on testing an individual's understanding of AI workloads, workload optimization, and the ability to develop AI applications using HPE Cray AI development environment.
HPE2-N69 exam is an excellent way for IT professionals to demonstrate their proficiency in using HPE Cray AI Development Environment, and it is a valuable certification to have for those who are interested in pursuing a career in artificial intelligence and machine learning.
NEW QUESTION # 13
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. Using hyperparameter optimization (HPO)
- C. Using a variable learning late
- D. Training the model on multiple epochs
Answer: D
NEW QUESTION # 14
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.
Answer: C
NEW QUESTION # 15
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. Minimizing costs in a cloud environment
- C. Equally distributing utilization across multiple agents
- D. Ensuring that the highest priority experiments obtain access to more resources
Answer: D
Explanation:
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.
NEW QUESTION # 16
You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use "det deploy" to set up the cluster.
What is one prerequisite?
- A. Manually creating the AWS EC2 instance with a PostgreSQL database
- B. installing the NVIDIA Container Toolkit on your local machine
- C. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
- D. Recording the name of a valid AWS EC2 keypair
Answer: B
NEW QUESTION # 17
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. Complex and time-consuming data cleansing process
- B. Complex model deployment processes
- C. Time-consuming data collection
- D. Complex and time-consuming hyperparameter optimization (HPO)
Answer: D
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 # 18
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 # 19
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: D
Explanation:
The WebUI for an HPE Machine Learning Development cluster can be accessed at the FQDN or IP address of the conductor. The conductor is responsible for managing the cluster and providing access to the WebUI.
NEW QUESTION # 20
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 HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
- 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 an HPE Machine Learning Development solution, and you should continue the discussion.
Answer: C
NEW QUESTION # 21
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. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- B. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- C. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- D. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
Answer: C
Explanation:
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.
NEW QUESTION # 22
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
- B. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- C. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- D. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
Answer: D
Explanation:
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.
NEW QUESTION # 23
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 conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
- C. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- D. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
Answer: A
NEW QUESTION # 24
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. It adds system and GPU monitoring to the training process.
- B. it helps engineers use a language like Python to code and trail DL models.
- C. it provides pipelines to manage the complete lifecycle.
- D. It is primarily used to transport trained models to a deployment environment.
Answer: C
Explanation:
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.
NEW QUESTION # 25
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 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 refers to testing how well the current model performs on new data; file lower the loss the better the performance.
Answer: D
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 # 26
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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
- B. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- 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
NEW QUESTION # 27
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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
- B. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- 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 # 28
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?
- A. It automatically cleans up data to create better end results.
- B. It helps DL projects complete faster for a faster ROI.
- C. It helps companies deploy models and generate revenue.
- D. It uses a centralized training architecture that is highly efficient.
Answer: B
NEW QUESTION # 29
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
- A. Models defined with Apache Spark rather than MapReduce
- B. Models that are trained through unsupervised, rather than supervised, training
- C. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers
- D. Models trained through multiple training processes implemented by different team members
Answer: C
Explanation:
Models based on neural networks with interconnected layers of nodes, including multiple hidden layers. Deep learning (DL) is a type of machine learning (ML) that uses models based on neural networks with interconnected layers of nodes, including multiple hidden layers. This is what distinguishes it from other forms of ML, which typically use simpler models with fewer layers. The multiple layers of DL models enable them to learn complex patterns and features from the data, allowing for more accurate and powerful predictions.
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 for "resources_per_trial?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
Explanation:
The valid setting for "resourcespertrial" for the 10 agents in "my-compute-poor" with 8 GPUs each would be 20, as this would be the total number of GPUs available across all 10 agents. This setting would allow the experiment config to run on multiple GPUs at once.
NEW QUESTION # 31
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
- B. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- C. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
- D. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
Answer: A
NEW QUESTION # 32
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. CPU-only Jupyter Notebook
- C. GPU Jupyter Notebook
- D. Validation
Answer: B
Explanation:
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.
NEW QUESTION # 33
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. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
- B. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- C. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- D. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
Answer: C
NEW QUESTION # 34
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. Re-enter the command with a period (.) at the end.
- C. Make sure that you have already logged into the cluster with the "det login'' command.
- D. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
Answer: D
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 # 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. Streaming requires just one bucket, while downloading requires many.
- B. The trial can more quickly start up and begin training the model.
- C. Setting up streaming is easier that setting up downloading.
- D. The trial can better separate training and validation data.
Answer: B
Explanation:
Streaming the data during a trial allows the data to be processed more quickly, as it does not need to be downloaded onto the cluster before training can begin. This means that the trial can start up faster and the model can begin training more quickly.
NEW QUESTION # 36
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)
Answer: D
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 # 37
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. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
- C. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- D. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
Answer: C
NEW QUESTION # 38
......
Free HPE2-N69 Exam Files Downloaded Instantly 100% Dumps & Practice Exam: https://interfacett.braindumpquiz.com/HPE2-N69-exam-material.html