HPE2-N69 Certification Exam Dumps Questions in here [Apr-2023]
Updated HPE2-N69 Exam Practice Test Questions
The HPE2-N69 exam, also known as the "Using HPE Cray AI Development Environment" exam, is an industry-recognized certification developed by Hewlett Packard Enterprise (HPE) for IT professionals who aspire to become AI developers. This certification validates the skills and knowledge required to develop and deploy AI applications using the HPE Cray AI Development Environment. Passing this exam demonstrates that the candidate has a comprehensive understanding of HPE Cray AI development tools, technologies, and methodologies.
The HP HPE2-N69 exam is designed to test the knowledge and skills of professionals who work with HPE Cray AI Development Environment. This exam is intended for individuals who want to demonstrate their expertise in developing and deploying AI applications using HPE Cray AI Development Environment. The exam covers a wide range of topics related to machine learning, data preparation, model training, deployment, and monitoring using HPE Cray AI Development Environment.
The HP HPE2-N69 certification exam consists of 60 multiple-choice questions that must be completed within 90 minutes. To pass the exam, candidates must achieve a score of 70% or higher. The exam covers various topics related to HPE Cray AI development environment, such as HPE Cray AI hardware and software components, programming models, data management, and deployment. Passing this certification exam demonstrates that the candidate has the knowledge and skills required to develop and implement efficient and scalable AI solutions using HPE Cray AI development environment.
NEW QUESTION # 22
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 trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- 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 # 23
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 # 24
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 uses a centralized training architecture that is highly efficient.
- C. It helps companies deploy models and generate revenue.
- D. It automatically cleans up data to create better end results.
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 # 25
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 weight to a higher than default value.
- C. Set the weight to a lower than default value.
- D. Set the priority to a lower than default value.
Answer: B
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 # 26
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. What frustrations do you have with existing ML deployment and differencing solutions?
- C. How long does it currently take for a DL training to run the backward pass?
- D. How much do you understand about building ML and DL models?
Answer: B
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 # 27
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 upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
- B. The engineer should run the "det preview-search" command, referencing the experiment config.
- 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 access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.
Answer: C
NEW QUESTION # 28
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 # 29
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Premium dedicated support
- B. Experiment tracking
- C. Distributed training
- D. Model Inferencing
Answer: B
NEW QUESTION # 30
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. The conductor's
- B. A virtual one assigned to the cluster
- C. Any of the agent's in an aux pool
- D. Any of the agent's in a compute pool
Answer: C
NEW QUESTION # 31
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. profiling: enabled: false
- C. hyperparameters; optimizer:none
- D. resources: slots_per_trial: 1
Answer: C
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 # 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 trained through multiple training processes implemented by different team members
- C. Models defined with Apache Spark rather than MapReduce
- 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 # 33
What are the mechanics of now a model trains?
- A. Tests how accurately the model performs on a wide array of real world data
- B. Detects Data drift of content drift that might compromise the ML model's performance
- 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 # 34
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. Data Center Bridging (OCB)-enabled Ethernet
- C. Slingshot
- 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 # 35
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 # 36
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Premium dedicated support
- B. Experiment tracking
- C. Model Inferencing
- D. Distributed training
Answer: D
Explanation:
The benefit of HPE Machine Learning Development Environment beyond open source Determined AI is Distributed Training. Distributed training allows multiple machines to train a single model in parallel, greatly increasing the speed and efficiency of the training process. HPE ML Development Environment provides tools and support for distributed training, allowing users to make the most of their resources and quickly train their models.
NEW QUESTION # 37
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: B
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. Streaming requires just one bucket, while downloading requires many.
- B. Setting up streaming is easier that setting up downloading.
- C. The trial can better separate training and validation data.
- D. The trial can more quickly start up and begin training the model.
Answer: C
NEW QUESTION # 39
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
NEW QUESTION # 40
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 how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- B. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- C. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
Answer: A
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 # 41
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. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- B. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- 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: B
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 # 42
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid training models to the point where they perform less well on new data.
- B. The team wants to spend less time on creating the code tor models and more time training models.
- C. The team wants to spend less time figuring out which CPUs are available for training models.
- D. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
Answer: C
NEW QUESTION # 43
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. CPU-only Jupyter Notebook
- B. Validation
- C. Training
- D. GPU Jupyter Notebook
Answer: A
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 # 44
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