[Oct 31, 2023] HPE2-N69 Exam Dumps - HP Practice Test Questions
New Real HPE2-N69 Exam Dumps Questions
NEW QUESTION # 14
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. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- 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. Re-enter the command with a period (.) at the end.
Answer: A
NEW QUESTION # 15
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 an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- C. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- D. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
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 # 16
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. K-12education
- C. Manufacturing
- D. Retail
Answer: C
Explanation:
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.
NEW QUESTION # 17
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 experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
- B. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- C. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
- D. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
Answer: C
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 # 18
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. Recording the name of a valid AWS EC2 keypair
- C. installing the NVIDIA Container Toolkit on your local machine
- D. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
Answer: C
NEW QUESTION # 19
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. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
- C. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- D. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
Answer: C
NEW QUESTION # 20
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Equally distributing utilization across multiple agents
- B. Ensuring that the highest priority experiments obtain access to more resources
- C. Ensuring that all experiments receive their fair share of resources
- D. Minimizing costs in a cloud environment
Answer: D
NEW QUESTION # 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 the "det preview-search" command, referencing the experiment config.
- 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 run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.
- D. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
Answer: B
Explanation:
The engineer should specify the number of trials to train on the max length and the minimum length for all trials in the experiment config file. For example, if the engineer wants to run 10 trials with a max length of 10, the config file should look something like this:
{
"mode": "A5HA",
"max_trials": 10,
"max_length": 10,
"min_length": 1,
"divisor": 2,
"max_runs": 1
}
Once the config file is complete, the engineer should upload it to the HPE Machine Learning Development Environment WebUI and view the graph of the experiment plan. This will allow the engineer to see how the Adaptive A5HA settings will affect the experiment. After that, the engineer can run the experiment and assess the results.
NEW QUESTION # 22
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. Adjusts the model's parameter weights such that the model can Better perform its tasks
- D. Decides which algorithm can best meet the use case for the application in question
Answer: D
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 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 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- D. Trial I is allowed to finish. Then Trial 3 is scheduled.
Answer: D
NEW QUESTION # 24
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
- 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. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
Answer: D
NEW QUESTION # 25
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: D
NEW QUESTION # 26
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Equally distributing utilization across multiple agents
- B. Minimizing costs in a cloud environment
- C. Ensuring that the highest priority experiments obtain access to more resources
- D. Ensuring that all experiments receive their fair share of resources
Answer: C
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 # 27
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. Validation
- B. CPU-only Jupyter Notebook
- C. Training
- D. GPU Jupyter Notebook
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 # 28
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 # 29
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It uploads model checkpoints.
- B. it downloads datasets for training.
- C. It ensures experiment metadata is stored.
- D. It validates trained models.
Answer: C
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 # 30
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- B. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- C. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- D. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
Answer: B
NEW QUESTION # 31
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Pipeline-based data management
- B. Automated hyperparameter optimization (HPO)
- C. Automated user provisioning
- D. Distributed training
Answer: A
NEW QUESTION # 32
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
- A. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers
- B. Models defined with Apache Spark rather than MapReduce
- C. Models that are trained through unsupervised, rather than supervised, training
- D. Models trained through multiple training processes implemented by different team members
Answer: C
NEW QUESTION # 33
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. Kubernetes
- B. Terralorm
- C. PyTorch
- D. Docker
Answer: D
Explanation:
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.
NEW QUESTION # 34
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. K-12education
- C. Manufacturing
- D. Retail
Answer: C
NEW QUESTION # 35
......
HP HPE2-N69 (Using HPE Cray AI Development Environment) Certification Exam is a highly specialized and comprehensive certification exam that is designed to test the knowledge and expertise of those who are interested in using the HPE Cray AI Development Environment. Using HPE Cray AI Development Environment certification exam is aimed at developers, data scientists, and other IT professionals who are looking to gain a deeper understanding of the HPE Cray AI Development Environment and its associated tools and technologies.
HPE Cray AI Development Environment is a comprehensive suite of tools that enables developers to build, test, and deploy AI applications with ease. This environment is designed to provide a complete end-to-end solution for AI development, including data processing, model training, and deployment. By obtaining the HPE2-N69 certification, candidates can demonstrate their proficiency in using this environment effectively to develop AI applications.
HPE2-N69 Certification Exam Dumps Questions in here: https://drive.google.com/open?id=131jiyChl-2sJ7q4eQpji3i4fxNw7c5Qc
Pass Your HPE2-N69 Exam Easily with Accurate PDF Questions: https://www.actualtests4sure.com/HPE2-N69-test-questions.html
