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HP HPE2-N69 Exam Syllabus Topics:

Topic	Details
Topic 1	Describe the HPE Machine Learning Development Environment software architecture and deployment options Have a conversation with customers about machine learning (ML) and deep learning (DL)
Topic 2	Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment Describe how HPE Machine Learning Development Environment fits in the market
Topic 3	Qualify customers for HPE Machine Learning Development Environment and System Articulate the business case for HPE Machine Learning Development solutions

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HP Using HPE Cray AI Development Environment Sample Questions (Q32-Q37):

NEW QUESTION # 32
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 tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
• C. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
• D. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.

Answer: D 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 # 33
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

• A. Distributed training
• B. Automated user provisioning
• C. Pipeline-based data management
• D. Automated hyperparameter optimization (HPO)

Answer: D 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 # 34
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

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

Answer: A
NEW QUESTION # 35
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 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 trial tails, and the ML engineer must restart it manually by re-running the experiment.

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 # 36
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 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 trial tails, and the ML engineer must restart it manually by re-running the experiment.

Answer: B
NEW QUESTION # 37
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