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The HPE Cray AI Development Environment is a comprehensive platform that enables organizations to build and deploy cutting-edge AI and ML applications. This platform provides a range of tools and frameworks to help developers build powerful and scalable AI solutions, including deep learning frameworks like TensorFlow and PyTorch. The HPE Cray AI Development Environment also includes a range of advanced features such as model optimization, automated hyperparameter tuning, and distributed training, which make it an ideal platform for building and deploying high-performance AI solutions. By earning the HP HPE2-N69 certification, professionals can demonstrate their proficiency in using the HPE Cray AI Development Environment and showcase their ability to deliver high-quality AI solutions.

The HP HPE2-N69 certification exam is designed for individuals who want to demonstrate their proficiency in using HPE Cray AI Development Environment. This exam covers various topics related to the HPE Cray AI Development Environment, including AI development, data preparation, and model training. The exam is ideal for software developers, data scientists, and IT professionals who want to enhance their skills in AI development.

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No doubt the HPE2-N69 Using HPE Cray AI Development Environment certification is one of the most challenging certification exams in the market. This HPE2-N69 Using HPE Cray AI Development Environment certification exam gives always a tough time to HPE2-N69 Using HPE Cray AI Development Environment exam candidates. The DumpsQuestion understands this hurdle and offers recommended and real HP HPE2-N69 exam practice questions in three different formats.

The HPE2-N69 exam covers a range of topics, including understanding the HPE Cray AI Development Environment architecture, deploying and configuring the platform, developing AI solutions using machine learning and deep learning algorithms, and integrating the platform with third-party tools and services. Passing the HPE2-N69 exam validates that the candidate has the knowledge and skills needed to develop and deploy AI solutions using HPE Cray AI Development Environment.

HP Using HPE Cray AI Development Environment Sample Questions (Q26-Q31):

NEW QUESTION # 26
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 "divisor- to 2 to reduce the share of the resource slots that the experiment receives.B. Under "resources.- set 'priority to I to reduce the share of the resource slots mat 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

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 # 27
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

A. The team wants to spend less time on creating the code tor models and more time training models.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 avoid wasting resources on training models with poorly selected hyperparameters.

Answer: C

NEW QUESTION # 28
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?

A. Experiment trackingB. Distributed trainingC. Model InferencingD. Premium dedicated support

Answer: A

NEW QUESTION # 29
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 hyperparameter optimization (HPO)B. Time-consuming data collectionC. Complex and time-consuming data cleansing processD. Complex model deployment processes

Answer: A

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 # 30
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. SlingshotC. Data Center Bridging (OCB)-enabled EthernetD. InfiniBand

Answer: C

NEW QUESTION # 31
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