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HPE2-N69 Exam Questions
- HPE Product Certified Certifications
- HP AI and Machine Learning [2022] Certifications
- Topic 1: Explain how HPE Machine Learning Development Environment helps customers surmount their challenges/ Run a proof of concept (PoC)
- Topic 2: Explain how the Machine Learning Development Environment uses resources and schedules workloads/ Understand the challenges customers face in training DL models
- Topic 3: Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment/ Describe how HPE Machine Learning Development Environment fits in the market
- Topic 4: Describe the HPE Machine Learning Development Environment software architecture and deployment options/ Have a conversation with customers about machine learning (ML) and deep learning (DL)
- Topic 5: Size HPE Machine Learning Development Environment and System solutions/ Understand machine learning (ML) and deep learning (DL) fundamentals
- Topic 6: Qualify customers for HPE Machine Learning Development Environment and System/ Articulate the business case for HPE Machine Learning Development solutions
- Topic 7: Demonstrate and explain how to use HPE Machine Learning Development Environment/ Describe the architecture for HPE Machine Learning Development solutions
Free HP HPE2-N69 Exam Actual Questions
Note: Premium Questions for HPE2-N69 were last updated On 30-07-2024 (see below)
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
The benefit of HPE Machine Learning Development Environment beyond open source Determined AI is Distributed Training. Distributed training allows multiple machines to train a single model in parallel, greatly increasing the speed and efficiency of the training process. HPE ML Development Environment provides tools and support for distributed training, allowing users to make the most of their resources and quickly train their models.
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?
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.
Refer to the exhibit.
You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what "validation loss' means. What should you respond?
Validation loss is a metric used to measure how well the model is performing on unseen data. It is calculated by taking the difference between the predicted values and the actual values. The lower the validation loss, the better the model's performance on new data.
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?
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.
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
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.
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