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NEW QUESTION # 24
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 complexity of adjusting model code to distribute the training process across multiple GPUs
- B. A lack of understanding of the DL model architecture by the NL engineering team
- C. A lack of adequate power and cooling for the GPU-enabled servers
- D. The requirement that the ML team must wait for the IT team to initiate each new training process
Answer: A
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 # 25
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 better separate training and validation data.
- C. The trial can more quickly start up and begin training the model.
- D. Setting up streaming is easier that setting up downloading.
Answer: B
NEW QUESTION # 26
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that the highest priority experiments obtain access to more resources
- B. Ensuring that all experiments receive their fair share of resources
- C. Minimizing costs in a cloud environment
- D. Equally distributing utilization across multiple agents
Answer: A
Explanation:
The best fit policy on an HPE Machine Learning Development Environment resource pool ensures that the highest priority experiments obtain access to more resources, while still ensuring that all experiments receive their fair share. This allows you to make the most of your resources and prioritize the experiments that are most important to you.
NEW QUESTION # 27
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
- A. A virtual one assigned to the cluster
- B. The conductor's
- C. Any of the agent's in an aux pool
- D. Any of the agent's in a compute pool
Answer: B
Explanation:
The WebUI for an HPE Machine Learning Development cluster can be accessed at the FQDN or IP address of the conductor. The conductor is responsible for managing the cluster and providing access to the WebUI.
NEW QUESTION # 28
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?
- A. It uses a centralized training architecture that is highly efficient.
- B. It helps DL projects complete faster for a faster ROI.
- C. It helps companies deploy models and generate revenue.
- D. It automatically cleans up data to create better end results.
Answer: B
Explanation:
HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.
NEW QUESTION # 29
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.
Answer: 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.
NEW QUESTION # 30
A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?
- A. Complex and time-consuming data cleansing process
- B. Complex model deployment processes
- C. Complex and time-consuming hyperparameter optimization (HPO)
- D. Time-consuming data collection
Answer: C
Explanation:
The HPE Machine Learning Development Environment specifically addresses Complex and time-consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.
NEW QUESTION # 31
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. it downloads datasets for training.
- B. It ensures experiment metadata is stored.
- C. It validates trained models.
- D. It uploads model checkpoints.
Answer: D
NEW QUESTION # 32
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 upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
- 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 run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.
- D. The engineer should run the "det preview-search" command, referencing the experiment config.
Answer: C
NEW QUESTION # 33
You are in a directory on your machine with your experiment config file and your model code. You enter this command:
det experiment create myfile.yaml
You receive this error:
det experiment create: error: the following arguments are required: model_def What should you do?
- A. Re-enter the command with a period (.) at the end.
- B. Re-enter the command with "-m" in which is the code filename.
- C. Make sure that you have already logged into the cluster with the "det login'' command.
- D. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
Answer: D
NEW QUESTION # 34
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. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account
- B. installing the NVIDIA Container Toolkit on your local machine
- C. Recording the name of a valid AWS EC2 keypair
- D. Manually creating the AWS EC2 instance with a PostgreSQL database
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.
NEW QUESTION # 35
An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO). What experiment config fields configure this behavior?
- A. hyperparameters; optimizer:none
- B. profiling: enabled: false
- C. searcher: name: single
- D. resources: slots_per_trial: 1
Answer: A
Explanation:
To train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO), you need to set the "optimizer" field to "none" in the hyperparameters section of the experiment config. This will instruct the ML engine to not use any hyperparameter optimization when training the model.
NEW QUESTION # 36
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
- B. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- C. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
- D. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
Answer: C
NEW QUESTION # 37
What distinguishes deep learning (DL) from other forms of machine learning (ML)?
- A. Models that are trained through unsupervised, rather than supervised, training
- B. Models defined with Apache Spark rather than MapReduce
- C. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers
- D. Models trained through multiple training processes implemented by different team members
Answer: A
NEW QUESTION # 38
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 the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- B. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- C. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
Answer: B
Explanation:
The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.
NEW QUESTION # 39
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They host management software such as the conductor and HPCM.
- B. They run non-distributed training workloads.
- C. They run validation and checkpoint workloads.
- D. They run training workloads that do not require GPUs.
Answer: A
NEW QUESTION # 40
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