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The HPE2-N69 certification exam covers a wide range of topics related to HPE Cray AI Development Environment, including data preparation, model creation, training, and deployment. The exam also focuses on the use of tools and techniques for optimizing the performance of machine learning and deep learning models. Additionally, the exam covers best practices for managing and monitoring machine learning and deep learning applications deployed on HPE Cray AI Development Environment.

HP Using HPE Cray AI Development Environment Sample Questions (Q31-Q36):

NEW QUESTION # 31
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

A. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.B. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.C. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.D. Implementing HPO manually can be time-consuming and demand a great deal of expertise.

Answer: B

NEW QUESTION # 32
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

A. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)B. SlingshotC. InfiniBandD. Data Center Bridging (OCB)-enabled Ethernet

Answer: A

Explanation:
HPE Machine Learning Development System uses Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE) for high-speed, agent-to-agent communications. This technology allows data to be transferred directly between agents without the need for copying, which results in improved performance and reduced latency.

NEW QUESTION # 33
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?

A. The requirement that the ML team must wait for the IT team to initiate each new training processB. The complexity of adjusting model code to distribute the training process across multiple GPUsC. A lack of understanding of the DL model architecture by the NL engineering teamD. A lack of adequate power and cooling for the GPU-enabled servers

Answer: 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.

NEW QUESTION # 34
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?

A. TrainingB. GPU Jupyter NotebookC. CPU-only Jupyter NotebookD. Validation

Answer: C

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.

NEW QUESTION # 35
What distinguishes deep learning (DL) from other forms of machine learning (ML)?

A. Models based on neural networks with interconnected layers of nodes, including multiple hidden layersB. Models trained through multiple training processes implemented by different team membersC. Models defined with Apache Spark rather than MapReduceD. Models that are trained through unsupervised, rather than supervised, training

Answer: D

NEW QUESTION # 36
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