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HP HPE2-N69 Exam Syllabus Topics:
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NEW QUESTION 20
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?
- A. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
- B. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
- C. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
- D. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
Answer: C
NEW QUESTION 21
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. it helps engineers use a language like Python to code and trail DL models.
- B. It is primarily used to transport trained models to a deployment environment.
- C. it provides pipelines to manage the complete lifecycle.
- D. It adds system and GPU monitoring to the training process.
Answer: A
NEW QUESTION 22
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. CPU-only Jupyter Notebook
- B. Validation
- C. Training
- D. GPU Jupyter Notebook
Answer: A
NEW QUESTION 23
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Experiment tracking
- B. Model Inferencing
- C. Premium dedicated support
- D. Distributed training
Answer: A
NEW QUESTION 24
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run validation and checkpoint workloads.
- B. They host management software such as the conductor and HPCM.
- C. They run non-distributed training workloads.
- D. They run training workloads that do not require GPUs.
Answer: B
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.
NEW QUESTION 25
You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?
- A. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
- B. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.
- C. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
- D. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
Answer: C
Explanation:
The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.
NEW QUESTION 26
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 27
Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment. What can you do?
- A. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- B. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
- C. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
- D. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
Answer: B
Explanation:
HPE Machine Learning Development Environment uses Amazon S3 to store checkpoints. To find the location of the best checkpoint created during an experiment, you need to look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID. This bucket will contain all of the checkpoints that were created during the experiment.
NEW QUESTION 28
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 model deployment processes
- B. Complex and time-consuming hyperparameter optimization (HPO)
- C. Complex and time-consuming data cleansing process
- D. Time-consuming data collection
Answer: C
NEW QUESTION 29
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 priority to a higher than default value.
- B. Set the priority to a lower than default value.
- C. Set the weight to a higher than default value.
- D. Set the weight to a lower than default value.
Answer: C
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.
NEW QUESTION 30
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. Setting up streaming is easier that setting up downloading.
- B. The trial can more quickly start up and begin training the model.
- C. Streaming requires just one bucket, while downloading requires many.
- D. The trial can better separate training and validation data.
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.
NEW QUESTION 31
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 model deployment processes
- B. Complex and time-consuming data cleansing process
- C. Time-consuming data collection
- D. Complex and time-consuming hyperparameter optimization (HPO)
Answer: D
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 32
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run validation and checkpoint workloads.
- B. They host management software such as the conductor and HPCM.
- C. They run non-distributed training workloads.
- D. They run training workloads that do not require GPUs.
Answer: B
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. A lack of understanding of the DL model architecture by the NL engineering team
- B. A lack of adequate power and cooling for the GPU-enabled servers
- C. The requirement that the ML team must wait for the IT team to initiate each new training process
- D. The complexity of adjusting model code to distribute the training process across multiple GPUs
Answer: A
NEW QUESTION 34
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?
- A. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- B. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
Answer: A
Explanation:
If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.
NEW QUESTION 35
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. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
- 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. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
Answer: D
NEW QUESTION 36
What are the mechanics of now a model trains?
- A. Adjusts the model's parameter weights such that the model can Better perform its tasks
- B. Detects Data drift of content drift that might compromise the ML model's performance
- C. Tests how accurately the model performs on a wide array of real world data
- D. Decides which algorithm can best meet the use case for the application in question
Answer: D
NEW QUESTION 37
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?
- A. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- B. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
- C. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- D. Trial I is allowed to finish. Then Trial 3 is scheduled.
Answer: C
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.
NEW QUESTION 38
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?
- A. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- B. Trial I is allowed to finish. Then Trial 3 is scheduled.
- C. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
- D. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
Answer: B
NEW QUESTION 39
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
- B. The team wants to spend less time figuring out which CPUs are available for training models.
- C. The team wants to spend less time on creating the code tor models and more time training models.
- D. The team wants to avoid training models to the point where they perform less well on new data.
Answer: D
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 40
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
- A. Red Hat 7-based Linux
- B. HP-UX v11i
- C. Windows 10 or above
- D. Windows Server 2016 or above
Answer: B
NEW QUESTION 41
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?
- A. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- B. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- C. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- D. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
Answer: B
Explanation:
By establishing multiple compute resource pools on the cluster, you can ensure that the correct servers are used for each experiment, depending on the number of GPUs required. This will help ensure that the experiments are run on the servers with the correct resources without having to manually assign each experiment to the appropriate server.
NEW QUESTION 42
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