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Exam ID : HPE2-N69
Exam type : Web based
Exam duration : 1 hour 30 minutes
Exam length : 40 questions
Passing score : 65%
Delivery languages : Japanese, English, Korean
Supporting resources : Using HPE AI and Machine Learning, Rev. 22.21

Ideal candidateThe ideal candidate for this exam includes those who will design and support solutions through the use of HPE Machine Learning Development Environment to easily implement and train machine learning models by removing complexities, optimizing cost, and accelerating innovation.

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Exam contents
This exam has 40 questions.
Here are types of questions to expect:
Multiple choice (multiple responses), scenario basedMultiple choice (single response), scenario basedMultiple choice (multiple responses)Multiple choice (single response)

Advice to help you take this exam
Complete the training and review all course materials and documents before you take the exam.
Exam items are based on expected knowledge acquired from job experience, an expected level of industry standard knowledge, or other prerequisites (events, supplemental materials, etc.).
Successful completion of the course or study materials alone, does not ensure you will pass the exam.

Read the entire question and consider all options before you answer. If the question includes an exhibit, study the exhibit and read the question again. Select the answer that fully responds to the question. If the question asks for more than one answer, select all correct answers. There is no partial credit.

Exam policies
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This exam validates that you can: Percentage of Exam

Sections/Objectives
24% Understand machine learning (ML) and deep learning (DL) fundamentals
1.1 Have a conversation with customers about machine learning (ML) and deep learning (DL)
1.2 Understand the challenges customers face in training DL models

13% Articulate the business case for HPE Machine Learning Development solutions
2.1 Explain how HPE Machine Learning Development Environment helps customers surmount their challenges
2.2 Describe how HPE Machine Learning Development Environment fits in the market

15% Describe the architecture for HPE Machine Learning Development solutions
3.1 Describe the HPE Machine Learning Development Environment software architecture and deployment options
3.2 Describe the HPE Machine Learning Development System

33% Demonstrate and explain how to use HPE Machine Learning Development Environment
4.1 Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment
4.2 Explain how the Machine Learning Development Environment uses resources and schedules workloads

15% Engage with customers
5.1 Qualify customers for HPE Machine Learning Development Environment and System
5.2 Size HPE Machine Learning Development Environment and System solutions
5.3 Run a proof of concept (PoC)

Sample questions are provided only as examples of question style, format and complexity/difficulty. They do not represent all question types and do not reflect all topic areas. These sample questions do not represent a practice test.
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Sample Questions

QUESTION 1
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 run the “det preview-search” command, referencing the experiment config.
B. The engineer should access the HPE Machine Learning Development online calculator and input
the mode, max_trials, max_length, divisor, and max_runs.
C. The engineer should upload the experiment config to the HPE Machine Learning Development
Environment WebUl and view the graph of the experiment plan.
D. 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.

Answer: B

Explanation:
The engineer should specify the number of trials to train on the max length and the minimum length
for all trials in the experiment config file. For example, if the engineer wants to run 10 trials with a
max length of 10, the config file should look something like this:
{
“mode”: “A5HA”,
“max_trials”: 10,
“max_length”: 10,
“min_length”: 1,
“divisor”: 2,
“max_runs”: 1
}
Once the config file is complete, the engineer should upload it to the HPE Machine Learning
Development Environment WebUI and view the graph of the experiment plan. This will allow the
engineer to see how the Adaptive A5HA settings will affect the experiment. After that, the engineer
can run the experiment and assess the results.

QUESTION 2
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 weight to a higher than default value.
B. Set the weight to a lower than default value.
C. Set the priority to a lower than default value.
D. Set the priority to a higher than default value.

Answer: A

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.

QUESTION 3
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. installing the NVIDIA Container Toolkit on your local machine
B. Manually creating the AWS EC2 instance with a PostgreSQL database
C. Recording the name of a valid AWS EC2 keypair
D. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account

Answer: C

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.

QUESTION 4
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. The trial can more quickly start up and begin training the model.
C. The trial can better separate training and validation data.
D. Setting up streaming is easier that setting up downloading.

Answer: B

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.