Latest [Jan 07, 2024] HPE2-N69 Exam Questions – Valid HPE2-N69 Dumps Pdf
HPE2-N69 Practice Test Questions Answers Updated 42 Questions
NEW QUESTION # 12
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. Validation
- C. CPU-only Jupyter Notebook
- D. Training
Answer: C
NEW QUESTION # 13
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. Decides which algorithm can best meet the use case for the application in question
- D. Adjusts the model's parameter weights such that the model can Better perform its tasks
Answer: D
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 # 14
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: 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 # 15
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 "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- B. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- C. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
- D. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
Answer: A
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 # 16
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 fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- C. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
- 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: B
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 # 17
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 trained through multiple training processes implemented by different team members
- D. Models that are trained through unsupervised, rather than supervised, training
Answer: A
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 # 18
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 the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- C. Re-enter the command with a period (.) at the end.
- D. Make sure that you have already logged into the cluster with the "det login'' command.
Answer: B
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 # 19
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 more quickly start up and begin training the model.
- B. The trial can better separate training and validation data.
- C. Streaming requires just one bucket, while downloading requires many.
- D. Setting up streaming is easier that setting up downloading.
Answer: B
NEW QUESTION # 20
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. A virtual one assigned to the cluster
- B. Any of the agent's in a compute pool
- C. The conductor's
- D. Any of the agent's in an aux pool
Answer: C
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 # 21
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 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- C. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- D. Trial I is allowed to finish. Then Trial 3 is scheduled.
Answer: B
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 # 22
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 validates trained models.
- C. It uploads model checkpoints.
- D. It ensures experiment metadata is stored.
Answer: C
NEW QUESTION # 23
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. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- D. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
Answer: B
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 # 24
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. Ensuring that the highest priority experiments obtain access to more resources
- D. Equally distributing utilization across multiple agents
Answer: B
NEW QUESTION # 25
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. Red Hat 7-based Linux
- B. Windows 10 or above
- C. HP-UX v11i
- D. Windows Server 2016 or above
Answer: A
Explanation:
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.
NEW QUESTION # 26
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. K-12education
- B. Hospitality
- C. Retail
- D. Manufacturing
Answer: D
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 # 27
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
Answer: B
NEW QUESTION # 28
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. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- B. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- 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: A
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 # 29
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