Using HPE AI and Machine Learning HPE2-N69 Exam Questions
Preparing for the HPE2-N69 exam is simple with Certs Vault. We offer easy-to-understand study materials that help you learn the most important exam topics. You can study using our PDF questions, practice online with a real exam-style test, or use the desktop practice software. Choose the study method that works best for you and prepare at your own pace.
At Certs Vault, we keep our HPE2-N69 practice questions up to date. Whenever the exam syllabus or objectives change, we update our study materials so you always learn the latest topics. This helps you save time, avoid outdated content, and feel more confident when you take your exam.
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?
Correct Answer: A
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.
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?
Correct Answer: A
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.
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
Correct Answer: C
Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.
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?
Correct Answer: D
Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
Correct Answer: B
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.