Questions for the HPE2-N69 were updated on : Dec 01 ,2025
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
C
Explanation:
Overfitting occurs when a model is trained too closely on the training data, leading to a model that
performs very well on the training data but poorly on new data. This is because the model has been
trained too closely to the training data, and so cannot generalize the patterns it has learned to new
data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on
the training data and that they have enough generalization capacity to be able to perform well on
new data.
What are the mechanics of now a model trains?
B
Explanation:
This is done by running the model through a training loop, where the model is fed data and the
parameter weights are adjusted based on the results of the model's performance on the data. For
example, if the model is a neural network, the weights of the connections between the neurons are
adjusted based on the results of the model's performance on the data. This process is repeated until
the model performs better on the data, at which point the model is considered trained.
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
A
Explanation:
Models based on neural networks with interconnected layers of nodes, including multiple hidden
layers. Deep learning (DL) is a type of machine learning (ML) that uses models based on neural
networks with interconnected layers of nodes, including multiple hidden layers. This is what
distinguishes it from other forms of ML, which typically use simpler models with fewer layers. The
multiple layers of DL models enable them to learn complex patterns and features from the data,
allowing for more accurate and powerful predictions.
A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?
B
Explanation:
The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep
learning (DL) training requires a large amount of computing power and can be accelerated by using
multiple GPUs. However, this requires adjusting the model code to distribute the training process
across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of
adjusting the model code is likely to continue to be a challenge in accelerating DL training.
ML engineers are defining a convolutional neural network (CNN) model bur they are not sure how
many filters to use in each convolutional layer. What can help them address this concern?
A
Explanation:
Hyperparameter optimization is a process of tuning the hyperparameters of a machine learning
model, such as the number of filters in a convolutional neural network (CNN) model, to determine
the best combination of hyperparameters that will result in the best model performance. HPO
techniques are used to automatically find the optimal hyperparameter values, which can greatly
increase the accuracy and performance of the model.
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?
D
Explanation:
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.
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
C
Explanation:
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 cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?
D
Explanation:
Pool 1 has two agents, each with 100 aux containers, and a total of 0 slots. This means that the
cluster is configured to run CPU-only workloads, such as running a CPU-only Jupyter Notebook.
Training, GPU Jupyter Notebook, and validation workloads cannot be run on this cluster due to the
lack of GPU resources.
You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for
the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the
cluster. What software must be installed on the machine before you run that command?
D
Explanation:
Before running the "del deploy" command to set up the cluster, you must first install Docker on the
machine. Docker is a containerization platform that is used to run applications in an isolated
environment. It is necessary to have Docker installed before running the "del deploy" command to
set up the cluster for the open source Determined AI on a local machine.
Where does TensorFlow fit in the ML/DL Lifecycle?
B
Explanation:
TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data
ingestion to model training, evaluation, and deployment. It helps engineers use a language like
Python to code and train DL models, and it also adds system and GPU monitoring to the training
process. Additionally, it can be used to transport trained models to a deployment environment.
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?
D
Explanation:
The customer is a key target for an HPE Machine Learning Development solution, and you should
continue the discussion. With the customer's dedicated IT staff, the customer is ready to deploy an
on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment
is a comprehensive solution that provides the tools and technologies required to develop, manage,
and deploy ML models. It includes a distributed training framework, an orchestration layer, a
powerful development environment, and an integrated MLOps platform. With this solution, the
customer can expand their ML/DL projects and scale up their team.
What is one key target vertical (or HPE Machine Learning Development solutions?
D
Explanation:
One key target vertical for HPE Machine Learning Development solutions is Manufacturing.
Manufacturing businesses are using machine learning to automate processes, reduce costs, and
improve safety and quality control. HPE ML solutions provide the tools and technologies to help
manufacturers develop and deploy ML models in their production environments, enabling them to
optimize and automate their operations.
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
C
Explanation:
HPE ProLiant DL325 servers play an important role in the HPE Machine Learning Development
System. They are used to host the management software such as the Conductor and HPCM, and they
also run non-distributed training workloads that do not require GPUs. They can also be used to run
validation and checkpoint workloads.
You want to open the conversation about HPE Machine Learning Development Environment with an
IT contact at a customer. What can be a good discovery question?
D
Explanation:
A good discovery question to start a conversation about HPE Machine Learning Development
Environment with an IT contact at a customer would be: "What frustrations do you have with existing
ML deployment and differencing solutions?" By understanding the customer's current challenges and
frustrations, you can better determine how HPE's ML Development Environment could help to
address those needs.
You are proposing an HPE Machine Learning Development Environment solution for a customer. On
what do you base the license count?
D
Explanation:
The license count for the HPE Machine Learning Development Environment solution would be based
on the number of processor cores on all servers in the cluster. This includes all servers in the cluster,
regardless of whether they are running agents or not. Each processor core in the cluster requires a
license and these licenses can be purchased in packs of 2, 4, 8, and 16.