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Access 42 expert-verified HPE2-N69 questions instantly through PDF, Desktop, or Online Test Engine formats. Actual4Labs is your dedicated partner for passing the HP Using HPE Cray AI Development Environment exam on your first attempt in 2026.
| Certification Vendor: | Hewlett Packard Enterprise (HPE) |
|---|---|
| Exam Name: | Using HPE AI and Machine Learning |
| Exam Number: | HPE2-N69 |
| Available Languages: | English, Korean, Japanese |
| Exam Format: | Multiple choice |
| Related Certifications: | HPE Machine Learning Development Environment HPE AI and Machine Learning Certification Track |
| Passing Score: | 65% |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 40 |
| Recommended Training: | HPE AI and Machine Learning Training (Official) |
| Exam Registration: | Pearson VUE HPE Exams HPE Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or Pearson VUE test center |
| Pre Condition: | No strict prerequisites (recommended basic ML and Linux/Python familiarity) |
| Official Syllabus URL: | https://certification-learning.hpe.com/tr/exams |
| Section | Weight | Objectives |
|---|---|---|
| Business Value of HPE ML Solutions | 13% | - Use cases and industry applications
|
| Machine Learning and Deep Learning Fundamentals | 24% | - Core ML concepts
|
| Using HPE Machine Learning Development Environment | 33% | - Experiment lifecycle
|
| Architecture of HPE Machine Learning Development Environment | 15% | - System architecture
|
| Customer Engagement and Solution Design | 15% | - Solution positioning
|
The HPE2-N69 exam is a core requirement for the HPE Machine Learning Development Environment Certification certification at the Foundational level. Earning this validates your skills for credentials like HPE Machine Learning Development Environment, HPE AI and Machine Learning Certification Track.
Candidates will face 40 questions and have 90 minutes to complete the exam. This means you need strict time management. We strongly recommend taking timed practice tests to ensure you can maintain a steady pace without rushing when under actual exam time pressure.
Candidate requirements include: No strict prerequisites (recommended basic ML and Linux/Python familiarity). Please verify all eligibility details on the official certification page before scheduling your appointment.
Registration is handled through official testing partners. The exam is delivered via Online proctored or Pearson VUE test center. You can sign up here:
The vendor suggests following official learning paths to build a foundational understanding:
Once you complete your training, use the 42 practice questions from Actual4Labs to refine your exam readiness.
Absolutely. A free PDF demo is available for you to evaluate the quality of our questions. Once purchased, you receive 365 days of free updates. If your access expires, you can renew it at a 50% discount to keep your materials current.
Your HPE2-N69 practice materials are delivered instantly. You can download them right away, and a copy will be sent to your email within one minute (contact support if not received in 2 hours). You can install the software on an unlimited number of computers. We also offer a 100% Money Back Guarantee: if you fail the corresponding exam within 60 days of purchase, you can claim a full refund. To apply, submit your enrollment slip and official Score Report PDF within 2 days after taking the test (processing takes up to 7 days). Note: failures within 3 days of purchase, downloaded but unattempted exams, free materials, and expired orders are excluded. The payer's name must match the candidate's name. Alternatively, you can exchange your order for two free exams of equal value and keep the original product's updates.
The syllabus is divided into 5 main domains. Key areas include "Architecture of HPE Machine Learning Development Environment" (15%), "Customer Engagement and Solution Design" (15%), "Business Value of HPE ML Solutions" (13%). For a comprehensive breakdown of all measured skills, please review the complete exam outline table provided above.
Question 1
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.
Question 2
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
A. Experiment tracking
B. Distributed training
C. Premium dedicated support
D. Model Inferencing
Question 3
What is one key target vertical (or HPE Machine Learning Development solutions?
A. K-12education
B. Retail
C. Hospitality
D. Manufacturing
Question 4
You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?
A. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
B. The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
C. The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
D. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
Question 5
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 wasting resources on training models with poorly selected hyperparameters.
C. The team wants to avoid training models to the point where they perform less well on new data.
D. The team wants to spend less time figuring out which CPUs are available for training models.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: C |
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