New HPE2-N69 Test Materials & Valid HPE2-N69 Test Engine
HPE2-N69 Updated Exam Dumps [2023] Practice Valid Exam Dumps Question
HP HPE2-N69 certification exam is designed for individuals who want to demonstrate their knowledge and skills in using the HPE Cray AI Development Environment. HPE2-N69 exam is intended for technical professionals who work with artificial intelligence (AI) and machine learning (ML) technologies and want to validate their expertise in using HPE Cray AI Development Environment to design, develop, and deploy AI and ML solutions.
NEW QUESTION # 21
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 figuring out which CPUs are available for training models.
- D. The team wants to spend less time on creating the code tor models and more time training models.
Answer: C
NEW QUESTION # 22
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: A
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 # 23
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 can handle hyperparameters related to neural architecture while ASHA cannot.
- C. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- D. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
Answer: D
NEW QUESTION # 24
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Experiment tracking
- C. Model Inferencing
- D. Premium dedicated support
Answer: B
NEW QUESTION # 25
A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?
- A. A lack of understanding of the DL model architecture by the NL engineering team
- B. A lack of adequate power and cooling for the GPU-enabled servers
- C. The requirement that the ML team must wait for the IT team to initiate each new training process
- D. The complexity of adjusting model code to distribute the training process across multiple GPUs
Answer: A
NEW QUESTION # 26
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 I is allowed to finish. Then Trial 3 is scheduled.
- D. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
Answer: C
NEW QUESTION # 27
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 weight to a higher than default value.
- B. Set the weight to a lower than default value.
- C. Set the priority to a higher than default value.
- D. Set the priority to a lower than default value.
Answer: A
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. 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
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
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Automated hyperparameter optimization (HPO)
- C. Pipeline-based data management
- D. Automated user provisioning
Answer: B
Explanation:
One of the main benefits of HPE Machine Learning Development Environment is its ability to automate the process of hyperparameter optimization (HPO). HPO is a process of automatically tuning the hyperparameters of a model during training, which can greatly improve a model's performance. HPE ML DE provides automated HPO, making the process of tuning and optimizing the model much easier and more efficient.
NEW QUESTION # 30
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 the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- C. Re-enter the command with "-m" in which is the code filename.
- D. Make sure that you have already logged into the cluster with the "det login'' command.
Answer: B
NEW QUESTION # 31
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. It adds system and GPU monitoring to the training process.
- B. It is primarily used to transport trained models to a deployment environment.
- C. it helps engineers use a language like Python to code and trail DL models.
- D. it provides pipelines to manage the complete lifecycle.
Answer: D
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 # 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. GPU Jupyter Notebook
- B. Training
- C. CPU-only Jupyter Notebook
- D. Validation
Answer: C
NEW QUESTION # 33
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 processor cores on agents
- B. The number of processor cores on all servers in the cluster
- C. The number of agent GPUs
- D. The number of servers in the cluster
Answer: C
NEW QUESTION # 34
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. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
- B. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- C. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.
- D. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
Answer: B
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 # 35
A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?
- A. A lack of adequate power and cooling for the GPU-enabled servers
- B. A lack of understanding of the DL model architecture by the NL engineering team
- C. The complexity of adjusting model code to distribute the training process across multiple GPUs
- D. The requirement that the ML team must wait for the IT team to initiate each new training process
Answer: C
Explanation:
The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.
NEW QUESTION # 36
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 figuring out which CPUs are available for training models.
- C. The team wants to spend less time on creating the code tor models and more time training models.
- D. The team wants to avoid training models to the point where they perform less well on new data.
Answer: D
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 # 37
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 an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- B. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
- C. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
- 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 # 38
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: A
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 # 39
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. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
- B. installing the NVIDIA Container Toolkit on your local machine
- C. Manually creating the AWS EC2 instance with a PostgreSQL database
- D. Recording the name of a valid AWS EC2 keypair
Answer: D
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 # 40
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run validation and checkpoint workloads.
- B. They run non-distributed training workloads.
- C. They run training workloads that do not require GPUs.
- D. They host management software such as the conductor and HPCM.
Answer: D
Explanation:
HPE ProLiant DL325 servers play an important role in the HPE Machine Learning Development System. They are used to host the management software such as the Conductor and HPCM, and they also run non-distributed training workloads that do not require GPUs. They can also be used to run validation and checkpoint workloads.
NEW QUESTION # 41
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It ensures experiment metadata is stored.
- B. It uploads model checkpoints.
- C. It validates trained models.
- D. it downloads datasets for training.
Answer: B
NEW QUESTION # 42
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 time do you spend managing the ML infrastructure?
- B. How much do you understand about building ML and DL models?
- C. How long does it currently take for a DL training to run the backward pass?
- D. What frustrations do you have with existing ML deployment and differencing solutions?
Answer: D
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 # 43
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. hyperparameters; optimizer:none
- B. profiling: enabled: false
- C. searcher: name: single
- D. resources: slots_per_trial: 1
Answer: A
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 # 44
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. Adjusts the model's parameter weights such that the model can Better perform its tasks
- C. Decides which algorithm can best meet the use case for the application in question
- D. Tests how accurately the model performs on a wide array of real world data
Answer: C
NEW QUESTION # 45
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. K-12education
- 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 # 46
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