Quality HPE2-N69 PDF Dumps - HPE2-N69 Exam Questions

Most UptoDate HP HPE2-N69 Exam Dumps PDF 2023

To sum it up, HPE2-N69: Using HPE Cray AI Development Environment exam provided by HPE is designed for professionals who aspire to enhance their skills in developing predictive applications and optimizing AI workloads using HPE Cray AI development environment. Using HPE Cray AI Development Environment certification validates an individual’s proficiency in building and optimizing AI workloads and lays a foundation for a successful career in the field of AI development and deployment.

 

NEW QUESTION # 14
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

• A. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
• B. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
• C. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
• D. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.

Answer: A

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 # 15
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

• A. It helps DL projects complete faster for a faster ROI.
• B. It helps companies deploy models and generate revenue.
• C. It automatically cleans up data to create better end results.
• D. It uses a centralized training architecture that is highly efficient.

Answer: A

Explanation:
HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.

NEW QUESTION # 16
You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?

• A. The number of agent GPUs
• B. The number of servers in the cluster
• C. The number of processor cores on all servers in the cluster
• D. The number of processor cores on agents

Answer: C

Explanation:
The license count for the HPE Machine Learning Development Environment solution would be based on the number of processor cores on all servers in the cluster. This includes all servers in the cluster, regardless of whether they are running agents or not. Each processor core in the cluster requires a license and these licenses can be purchased in packs of 2, 4, 8, and 16.

NEW QUESTION # 17
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

• A. It validates trained models.
• B. It uploads model checkpoints.
• C. it downloads datasets for training.
• D. It ensures experiment metadata is stored.

Answer: B

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. Recording the name of a valid AWS EC2 keypair
• B. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
• C. installing the NVIDIA Container Toolkit on your local machine
• D. Manually creating the AWS EC2 instance with a PostgreSQL database

Answer: A

Explanation:
In order to use the "det deploy" command to set up a cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS), you will need to have a valid AWS EC2 keypair. The keypair will authenticate your access to the cluster and allow you to securely access the cluster once it is set up.

NEW QUESTION # 19
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 model deployment processes
• C. Complex and time-consuming data cleansing process
• 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 # 20
What is the role of a hidden layer in an artificial neural network (ANN)?

• A. It is responsible for passively reformatting data for use in the ANN.
• B. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
• C. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
• D. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.

Answer: D

NEW QUESTION # 21
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. Streaming requires just one bucket, while downloading requires many.
• C. The trial can more quickly start up and begin training the model.
• D. Setting up streaming is easier that setting up downloading.

Answer: C

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 # 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. Complex and time-consuming data cleansing process
• B. Time-consuming data collection
• C. Complex model deployment processes
• D. Complex and time-consuming hyperparameter optimization (HPO)

Answer: A

NEW QUESTION # 23
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 # 24
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 # 25
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. searcher: name: single
• B. resources: slots_per_trial: 1
• C. profiling: enabled: false
• D. hyperparameters; optimizer:none

Answer: D

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 # 26
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. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
• D. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type

Answer: A

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. Training
• B. Validation
• C. GPU Jupyter Notebook
• D. CPU-only Jupyter Notebook

Answer: D

NEW QUESTION # 28
What is the role of a hidden layer in an artificial neural network (ANN)?

• A. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
• B. It is responsible for passively reformatting data for use in the ANN.
• C. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
• D. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.

Answer: A

Explanation:
A hidden layer in an artificial neural network (ANN) is responsible for receiving and weighing inputs from the preceding layer and producing outputs for the next layer. It is also responsible for reformatting data for use in the ANN and helps to optimize the ANN during the backward pass.

NEW QUESTION # 29
Where does TensorFlow fit in the ML/DL Lifecycle?

• A. it helps engineers use a language like Python to code and trail DL models.
• B. It adds system and GPU monitoring to the training process.
• C. it provides pipelines to manage the complete lifecycle.
• D. It is primarily used to transport trained models to a deployment environment.

Answer: A

NEW QUESTION # 30
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 conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
• 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: 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 # 31
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 # 32
A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

• A. Set the priority to a higher than default value.
• B. Set the priority to a lower than default value.
• C. Set the weight to a lower than default value.
• D. Set the weight to a higher than default value.

Answer: D

Explanation:
Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.

NEW QUESTION # 33
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. Streaming requires just one bucket, while downloading requires many.
• C. The trial can better separate training and validation data.
• D. Setting up streaming is easier that setting up downloading.

Answer: C

NEW QUESTION # 34
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 solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
• C. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
• D. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.

Answer: A

NEW QUESTION # 35
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