HPE2-N69 Certification Overview - [Jul 29, 2023] Latest HPE2-N69 PDF Dumps
The Best HP HPE2-N69 Study Guides and Dumps of 2023
HPE2-N69 exam covers a range of topics related to developing AI solutions using HPE Cray technology. This includes understanding the HPE Cray AI development environment, configuring and managing the environment, building and training AI models, deploying and managing AI models, and troubleshooting common issues. HPE2-N69 exam is designed to validate a candidate's knowledge and skills in these areas, ensuring they are fully equipped to develop AI solutions using HPE Cray technology.
NEW QUESTION # 24
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 concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- C. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
- D. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
Answer: B
NEW QUESTION # 25
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. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- B. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- C. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
- D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
Answer: A
NEW QUESTION # 26
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 how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
- B. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
- C. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
- D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.
Answer: C
NEW QUESTION # 27
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
- B. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
- C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- D. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
Answer: D
Explanation:
Adaptive ASHA is an enhanced version of ASHA that uses a reinforcement learning approach to select hyperparameter configurations. This allows Adaptive ASHA to select higher-performing configs and clone those configurations, allowing for better performance than ASHA.
NEW QUESTION # 28
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 lower than default value.
- B. Set the priority to a higher than default value.
- C. Set the weight to a lower than default value.
- D. Set the weight to a higher than default value.
Answer: D
NEW QUESTION # 29
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 # 30
What is the role of a hidden layer in an artificial neural network (ANN)?
- A. It is responsible for passively reformatting data for use in the ANN.
- B. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
- C. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
- D. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
Answer: B
NEW QUESTION # 31
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 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- B. Trial I is allowed to finish. Then Trial 3 is scheduled.
- C. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- D. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
Answer: A
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 # 32
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Hospitality
- B. Manufacturing
- C. Retail
- D. K-12education
Answer: B
NEW QUESTION # 33
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
- A. It uploads model checkpoints.
- B. it downloads datasets for training.
- C. It validates trained models.
- D. It ensures experiment metadata is stored.
Answer: D
Explanation:
The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.
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. GPU Jupyter Notebook
- B. Training
- C. Validation
- D. CPU-only Jupyter Notebook
Answer: D
NEW QUESTION # 35
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 # 36
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 layers
- B. Models that are trained through unsupervised, rather than supervised, training
- C. Models defined with Apache Spark rather than MapReduce
- D. Models trained through multiple training processes implemented by different team members
Answer: 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.
NEW QUESTION # 37
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. Using hyperparameter optimization (HPO)
- B. Using a variable learning late
- C. Training the model on multiple epochs
- D. Distributing the training across multiple CPUs
Answer: C
NEW QUESTION # 38
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. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
- B. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
- C. Use the "det experiment download -top-n I" command, referencing the experiment ID.
- D. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.
Answer: C
NEW QUESTION # 39
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?
- A. How long does it currently take for a DL training to run the backward pass?
- B. How much time do you spend managing the ML infrastructure?
- C. How much do you understand about building ML and DL models?
- D. What frustrations do you have with existing ML deployment and differencing solutions?
Answer: A
NEW QUESTION # 40
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
- A. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
- B. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
- C. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
- D. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.
Answer: D
NEW QUESTION # 41
......
Valid HPE2-N69 Exam Updates - 2023 Study Guide: https://passleader.dumpexams.com/HPE2-N69-vce-torrent.html