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NVIDIA NCA-AIIO Bundle

Exam Code: NCA-AIIO

Exam Name NCA - AI Infrastructure and Operations

Certification Provider: NVIDIA

Corresponding Certification: NCA-AIIO

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NVIDIA NCA-AIIO Practice Test 28

Question 270

During a high-traffic model-service launch at an online commerce company, engineers report the following issue: A procurement team sizes GPUs only from compressed model file size. The affected work supports product search, recommendations and fraud screening; peak request demand makes degraded predictions visible to customers. Select TWO: the most appropriate action and the strongest verification of that action. (Select TWO.)

  1. A measured offload configuration performs the intended network or security service while preserving application CPU resources and policy.
  2. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.
  3. Calculate steady and peak rack power demand for GPU servers, network devices and support equipment, with appropriate electrical headroom and redundant feed design.
  4. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  5. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Explanation

Correct Answers

B:

Correct corrective action. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This fits the specific reported issue: A procurement team sizes GPUs only from compressed model file size. The corresponding failure being reduced is: Parameter count alone understates training memory and can cause out-of-memory failures even if nominal GPU compute is sufficient.

E:

Correct independent evidence. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure. This verifies the corrective approach for the affected product search, recommendations and fraud screening instead of treating an architecture diagram alone as success.

Incorrect Answers

A:

Not the most direct the action-and-evidence pair for these facts. A measured offload configuration performs the intended network or security service while preserving application CPU resources and policy. This choice concerns bluefield dpu infrastructure offload and may be valid in another design. But this case reports 'A procurement team sizes GPUs only from compressed model file size', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the action-and-evidence pair for these facts. Calculate steady and peak rack power demand for GPU servers, network devices and support equipment, with appropriate electrical headroom and redundant feed design. This choice concerns ai rack power budgeting and delivery and may be valid in another design. But this case reports 'A procurement team sizes GPUs only from compressed model file size', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the action-and-evidence pair for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'A procurement team sizes GPUs only from compressed model file size', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 271

During a regulated deployment assessment at a healthcare imaging provider, engineers report the following issue: Training succeeds with batch one but fails when the batch increases. The affected work supports clinical imaging and model-assisted workflows; patient data constraints and reliable model performance are important. Which action most directly addresses the described requirement?

  1. Size east-west bandwidth, latency and oversubscription for distributed GPU training based on communication patterns rather than only user-facing north-south traffic.
  2. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.
  3. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  4. Consider data residency, security requirements, network egress and operational control when deciding between private infrastructure, cloud and a hybrid AI environment.

Explanation

Correct Answer

B:

Correct. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This directly addresses the observed issue: Training succeeds with batch one but fails when the batch increases. The important distinction is gpu memory capacity for model training, not a substitute feature from a different component. The follow-up validation is: A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Incorrect Answers

A:

Not the most direct the corrective action for these facts. Size east-west bandwidth, latency and oversubscription for distributed GPU training based on communication patterns rather than only user-facing north-south traffic. This choice concerns east-west ai data center network requirements and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the corrective action for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the corrective action for these facts. Consider data residency, security requirements, network egress and operational control when deciding between private infrastructure, cloud and a hybrid AI environment. This choice concerns data locality and control in ai deployment placement and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 272

During a regulated deployment assessment at a healthcare imaging provider, engineers report the following issue: Training succeeds with batch one but fails when the batch increases. The affected work supports clinical imaging and model-assisted workflows; patient data constraints and reliable model performance are important. Which acceptance result is most relevant to this workload?

  1. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.
  2. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job.
  3. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system.
  4. A supported RDMA path passes connectivity and performance checks and demonstrates the intended transport rather than a TCP fallback.

Explanation

Correct Answer

A:

Correct. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure. This is observable evidence for the issue 'Training succeeds with batch one but fails when the batch increases' in clinical imaging and model-assisted workflows. It tests whether the chosen action works under the relevant operating conditions, rather than merely showing that software was installed or a server is powered on.

Incorrect Answers

B:

Not the most direct the acceptance evidence for these facts. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the acceptance evidence for these facts. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system. This choice concerns rack floor loading and physical space readiness and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the acceptance evidence for these facts. A supported RDMA path passes connectivity and performance checks and demonstrates the intended transport rather than a TCP fallback. This choice concerns rdma direct memory transport concepts and may be valid in another design. But this case reports 'Training succeeds with batch one but fails when the batch increases', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 273

During an industrial computer-vision rollout at a manufacturing operator, engineers report the following issue: A project budgets GPU arithmetic throughput without estimating activations. The affected work supports factory cameras and maintenance analytics; downtime and incorrect detections can interrupt production. What should the AI infrastructure engineer recommend first?

  1. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  2. Match cooling capacity, airflow direction and rack placement to the heat output of concentrated GPU systems, using measured inlet temperatures and facility constraints.
  3. Compare public-cloud GPU elasticity, service availability, transfer cost and operating expense with owned cluster utilization and lifecycle cost before choosing placement.
  4. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Explanation

Correct Answer

D:

Correct. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This directly addresses the observed issue: A project budgets GPU arithmetic throughput without estimating activations. The important distinction is gpu memory capacity for model training, not a substitute feature from a different component. The follow-up validation is: A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Incorrect Answers

A:

Not the most direct the corrective action for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

B:

Not the most direct the corrective action for these facts. Match cooling capacity, airflow direction and rack placement to the heat output of concentrated GPU systems, using measured inlet temperatures and facility constraints. This choice concerns cooling design for dense accelerated racks and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the corrective action for these facts. Compare public-cloud GPU elasticity, service availability, transfer cost and operating expense with owned cluster utilization and lifecycle cost before choosing placement. This choice concerns cloud elasticity versus on-prem capacity tradeoffs and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 274

During an industrial computer-vision rollout at a manufacturing operator, engineers report the following issue: A project budgets GPU arithmetic throughput without estimating activations. The affected work supports factory cameras and maintenance analytics; downtime and incorrect detections can interrupt production. Which observed result would justify closing this technical concern?

  1. The selected placement demonstrably meets allowed data locations, access controls, connectivity and realistic transfer times.
  2. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job.
  3. An end-to-end cost and capacity study covers representative utilization, deployment lead time, data movement and required service levels.
  4. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Explanation

Correct Answer

D:

Correct. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure. This is observable evidence for the issue 'A project budgets GPU arithmetic throughput without estimating activations' in factory cameras and maintenance analytics. It tests whether the chosen action works under the relevant operating conditions, rather than merely showing that software was installed or a server is powered on.

Incorrect Answers

A:

Not the most direct the acceptance evidence for these facts. The selected placement demonstrably meets allowed data locations, access controls, connectivity and realistic transfer times. This choice concerns data locality and control in ai deployment placement and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

B:

Not the most direct the acceptance evidence for these facts. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the acceptance evidence for these facts. An end-to-end cost and capacity study covers representative utilization, deployment lead time, data movement and required service levels. This choice concerns cloud elasticity versus on-prem capacity tradeoffs and may be valid in another design. But this case reports 'A project budgets GPU arithmetic throughput without estimating activations', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 275

During a security and capacity review at a financial analytics firm, engineers report the following issue: An engineering team compares two accelerator models while ignoring their usable memory. The affected work supports risk scoring and historical-data models; traceable controls and predictable throughput are required. Which design choice best resolves this technical constraint?

  1. Associate a BlueField DPU with offloading selected networking, storage and security infrastructure work from host CPUs while meeting supported deployment constraints.
  2. Differentiate high-bandwidth local GPU connections such as NVLink or NVSwitch from rack and cluster networking used between servers.
  3. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  4. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Explanation

Correct Answer

D:

Correct. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This directly addresses the observed issue: An engineering team compares two accelerator models while ignoring their usable memory. The important distinction is gpu memory capacity for model training, not a substitute feature from a different component. The follow-up validation is: A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Incorrect Answers

A:

Not the most direct the corrective action for these facts. Associate a BlueField DPU with offloading selected networking, storage and security infrastructure work from host CPUs while meeting supported deployment constraints. This choice concerns bluefield dpu infrastructure offload and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

B:

Not the most direct the corrective action for these facts. Differentiate high-bandwidth local GPU connections such as NVLink or NVSwitch from rack and cluster networking used between servers. This choice concerns nvlink scale-up versus external network scale-out and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the corrective action for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 276

During a security and capacity review at a financial analytics firm, engineers report the following issue: An engineering team compares two accelerator models while ignoring their usable memory. The affected work supports risk scoring and historical-data models; traceable controls and predictable throughput are required. How should the team prove that the requested outcome is achieved?

  1. Under representative concurrency, network counters and collective benchmarks show acceptable throughput, latency and congestion.
  2. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.
  3. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job.
  4. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system.

Explanation

Correct Answer

B:

Correct. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure. This is observable evidence for the issue 'An engineering team compares two accelerator models while ignoring their usable memory' in risk scoring and historical-data models. It tests whether the chosen action works under the relevant operating conditions, rather than merely showing that software was installed or a server is powered on.

Incorrect Answers

A:

Not the most direct the acceptance evidence for these facts. Under representative concurrency, network counters and collective benchmarks show acceptable throughput, latency and congestion. This choice concerns east-west ai data center network requirements and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the acceptance evidence for these facts. GPU duty-cycle telemetry and input pipeline measurements show the intended sustained utilization under a representative training job. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the acceptance evidence for these facts. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system. This choice concerns rack floor loading and physical space readiness and may be valid in another design. But this case reports 'An engineering team compares two accelerator models while ignoring their usable memory', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 277

During a regional deployment expansion at a media technology business, engineers report the following issue: Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks. The affected work supports language and visual-content AI services; different workloads compete for shared acceleration and storage. What implementation is most defensible given the stated facts?

  1. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  2. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.
  3. Calculate steady and peak rack power demand for GPU servers, network devices and support equipment, with appropriate electrical headroom and redundant feed design.
  4. Use supported DPU infrastructure functions to separate privileged platform networking or security services from untrusted host workload control when appropriate.

Explanation

Correct Answer

B:

Correct. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This directly addresses the observed issue: Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks. The important distinction is gpu memory capacity for model training, not a substitute feature from a different component. The follow-up validation is: A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Incorrect Answers

A:

Not the most direct the corrective action for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the corrective action for these facts. Calculate steady and peak rack power demand for GPU servers, network devices and support equipment, with appropriate electrical headroom and redundant feed design. This choice concerns ai rack power budgeting and delivery and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the corrective action for these facts. Use supported DPU infrastructure functions to separate privileged platform networking or security services from untrusted host workload control when appropriate. This choice concerns dpu isolation and secure infrastructure services and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 278

During a regional deployment expansion at a media technology business, engineers report the following issue: Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks. The affected work supports language and visual-content AI services; different workloads compete for shared acceleration and storage. What is the primary unresolved risk implied by this situation?

  1. Parameter count alone understates training memory and can cause out-of-memory failures even if nominal GPU compute is sufficient.
  2. Hotspots, inadequate airflow or insufficient liquid-cooling capacity can cause thermal throttling and hardware availability issues.
  3. Expensive GPUs can idle waiting for data or parameter exchange when the surrounding system is inadequately provisioned.
  4. An ordinary enterprise access network can become the bottleneck for GPU collectives and synchronized training.

Explanation

Correct Answer

A:

Correct. Parameter count alone understates training memory and can cause out-of-memory failures even if nominal GPU compute is sufficient. The observed condition 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks' creates this exposure. The intended mitigation is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Incorrect Answers

B:

Not the most direct the primary risk for these facts. Hotspots, inadequate airflow or insufficient liquid-cooling capacity can cause thermal throttling and hardware availability issues. This choice concerns cooling design for dense accelerated racks and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the primary risk for these facts. Expensive GPUs can idle waiting for data or parameter exchange when the surrounding system is inadequately provisioned. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the primary risk for these facts. An ordinary enterprise access network can become the bottleneck for GPU collectives and synchronized training. This choice concerns east-west ai data center network requirements and may be valid in another design. But this case reports 'Fine-tuning introduces optimizer and gradient allocations absent from serving benchmarks', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 279

During a shared-cluster operating review at a university HPC center, engineers report the following issue: A distributed training job fails at startup because per-device memory requirements were not modeled. The affected work supports research training and scheduled compute allocations; fair resource access and repeatable cluster operations matter. Which approach targets the actual problem rather than a different AI issue?

  1. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  2. Use supported DPU infrastructure functions to separate privileged platform networking or security services from untrusted host workload control when appropriate.
  3. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.
  4. Plan multiple accelerator nodes with a suitable high-bandwidth low-latency network and distributed training communications, then measure scaling efficiency.

Explanation

Correct Answer

C:

Correct. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This directly addresses the observed issue: A distributed training job fails at startup because per-device memory requirements were not modeled. The important distinction is gpu memory capacity for model training, not a substitute feature from a different component. The follow-up validation is: A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.

Incorrect Answers

A:

Not the most direct the corrective action for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

B:

Not the most direct the corrective action for these facts. Use supported DPU infrastructure functions to separate privileged platform networking or security services from untrusted host workload control when appropriate. This choice concerns dpu isolation and secure infrastructure services and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

D:

Not the most direct the corrective action for these facts. Plan multiple accelerator nodes with a suitable high-bandwidth low-latency network and distributed training communications, then measure scaling efficiency. This choice concerns scale-out cluster design and distributed communication and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

Question 280

During a shared-cluster operating review at a university HPC center, engineers report the following issue: A distributed training job fails at startup because per-device memory requirements were not modeled. The affected work supports research training and scheduled compute allocations; fair resource access and repeatable cluster operations matter. Choose TWO: one corrective approach and one result demonstrating it works. (Select TWO.)

  1. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication.
  2. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.
  3. Design a cluster as an integrated compute, storage, networking, orchestration and management system rather than an unordered collection of GPU servers.
  4. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure.
  5. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system.

Explanation

Correct Answers

B:

Correct corrective action. Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware. This fits the specific reported issue: A distributed training job fails at startup because per-device memory requirements were not modeled. The corresponding failure being reduced is: Parameter count alone understates training memory and can cause out-of-memory failures even if nominal GPU compute is sufficient.

D:

Correct independent evidence. A representative training step fits available memory with headroom and records memory peaks rather than relying only on a vendor peak-operations figure. This verifies the corrective approach for the affected research training and scheduled compute allocations instead of treating an architecture diagram alone as success.

Incorrect Answers

A:

Not the most direct the action-and-evidence pair for these facts. Size accelerator throughput alongside CPU preprocessing, storage read bandwidth and networking so the training system can sustain data delivery and communication. This choice concerns training system compute and data feed balance and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

C:

Not the most direct the action-and-evidence pair for these facts. Design a cluster as an integrated compute, storage, networking, orchestration and management system rather than an unordered collection of GPU servers. This choice concerns core components of an accelerated ai cluster and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.

E:

Not the most direct the action-and-evidence pair for these facts. Facilities approve actual floor loading, cabinet clearances and installation handling requirements for the proposed system. This choice concerns rack floor loading and physical space readiness and may be valid in another design. But this case reports 'A distributed training job fails at startup because per-device memory requirements were not modeled', which calls for reasoning about gpu memory capacity for model training. The targeted response is: Estimate model weights, gradients, optimizer state, activations and batch size against usable GPU memory before selecting training hardware.