NCP-GENL Fine-Tuning — All Questions With Answers

Question 1hardmultiple choice

Refer to the exhibit. Which adjustment should you make to the configuration if the model shows signs of overfitting on a small, domain-specific dataset?

Exhibit

Config: { "lora_r": 64, "lora_alpha": 16, "target_modules": ["q_proj", "v_proj"], "task_type": "CAUSAL_LM" }

Question 2mediummultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. Which strategy is most effective to resolve the OOM error while maintaining the 70B parameter model's performance?

Exhibit

Error Log: RuntimeError: CUDA error: out of memory. GPU has 24GB. Model: 70B parameter. Optimizer: AdamW (32-bit).

Question 3mediummulti select

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Which TWO metrics are most appropriate for evaluating the quality of a fine-tuned model for a creative writing task? (Choose two)

Question 4hardmultiple choice

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Which TWO of the following strategies are most effective for preventing catastrophic forgetting when fine-tuning a model for a new specialized domain?

Question 5mediummultiple choice

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When preparing a dataset for supervised fine-tuning (SFT), which THREE practices ensure high-quality model outcomes?

Question 6mediummultiple choice

Read the full network assurance explanation →

An enterprise AI engineer is preparing to fine-tune a large language model using NVIDIA NeMo. The training dataset contains highly confidential internal telemetry logs that must not be exposed to the underlying foundation model weights permanently or leaked during evaluation. Which specialized fine-tuning technique should be selected to minimize parameter modification while preserving core base model weights?

Question 7mediummultiple choice

Read the full Fine-Tuning explanation →

An enterprise AI team is preparing to fine-tune a large language model using NVIDIA NeMo. They want to ensure maximum training stability and avoid vanishing gradients when scaling up to multi-node clusters. Which activation checkpointing strategy should be configured?

Question 8hardmulti select

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When configuring Parameter-Efficient Fine-Tuning (PEFT) using LoRA within the NVIDIA NeMo framework, which TWO parameters are critical to adjust for balancing adaptation capacity and GPU memory consumption?

Question 9hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer is fine-tuning a 13B parameter model using NVIDIA NeMo Framework with tensor parallelism (TP) of 4 and pipeline parallelism (PP) of 2 on 8 A100 GPUs. They want to maximize throughput while maintaining convergence. They notice that increasing the micro-batch size beyond 4 causes an out-of-memory error, but GPU utilization remains low. Which configuration change should they make to improve throughput without increasing memory usage?

Question 10mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter LLM with NVIDIA NeMo on a node of eight A100 80GB GPUs. They want to maximize training throughput while keeping the global batch size fixed and avoiding GPU memory errors. Which NeMo configuration change is most appropriate?

Question 11mediummulti select

Read the full Fine-Tuning explanation →

A team is adapting a 70B model with LoRA inside NVIDIA NeMo and wants the adapter to learn task-specific behavior without disturbing the frozen base weights. Which two configuration choices support this goal? (Choose two.)

Question 12mediummultiple choice

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When fine-tuning a Large Language Model using Low-Rank Adaptation (LoRA), which architectural component is primarily modified to reduce computational overhead while maintaining performance?

Question 13mediummultiple choice

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When using QLoRA for fine-tuning, what is the primary purpose of using the 4-bit NormalFloat (NF4) data type?

Question 14hardmulti select

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Which TWO of the following practices are considered standard procedures for preparing a dataset for Instruction Fine-Tuning (IFT)? (Choose two)

Question 15mediummultiple choice

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What is the primary risk of 'catastrophic forgetting' during the fine-tuning process?

Question 16hardmulti select

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Which THREE factors significantly influence the memory consumption during LLM fine-tuning? (Choose three)

Question 17easymultiple choice

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What is the primary function of the 'rank' parameter in LoRA?

Question 18mediummultiple choice

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Why is gradient checkpointing useful when fine-tuning a model on a single GPU?

Question 19hardmultiple choice

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Refer to the exhibit. What is the effective batch size for this fine-tuning job?

Exhibit

Config: { "gradient_accumulation_steps": 16, "per_device_train_batch_size": 1 }

Question 20mediummultiple choice

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Which of the following describes the purpose of a 'System Prompt' in Instruction Fine-Tuning?

Question 21mediummultiple choice

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What is the primary role of the 'Learning Rate Scheduler' during LLM fine-tuning?

Question 22hardmultiple choice

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Refer to the exhibit. In the context of a distributed multi-GPU fine-tuning job, what is the most likely cause of this error?

Exhibit

Error Log: [NCCL WARN] Net : Connection refused. Check network configuration.

Question 23mediummultiple choice

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An enterprise is fine-tuning a large language model using NVIDIA NeMo Framework and encounters GPU out-of-memory errors during the backward pass. The training configuration already uses mixed-precision training (FP16). Which architectural intervention should be applied to resolve memory pressure while retaining the optimizer state precision?

Question 24mediummultiple choice

Read the full Fine-Tuning explanation →

When fine-tuning a base LLM using Parameter-Efficient Fine-Tuning (PEFT) on an NVIDIA H100 GPU, what is the primary advantage of utilizing LoRA compared to full fine-tuning?

Question 25hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. An engineer observes that a model fine-tuned with this LoRA configuration is failing to converge on a highly complex legal document domain. What is the most likely cause of this issue?

Exhibit

config: { "rank": 8, "alpha": 16, "target_modules": ["query_key_value", "dense"], "dropout": 0.05, "bias": "none" }

Question 26mediummultiple choice

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In the context of NVIDIA NeMo, why is it recommended to use FP8 precision during the fine-tuning process on H100 GPUs?

Question 27mediummultiple choice

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Which metric is the most reliable indicator that an LLM is overfitting during the fine-tuning phase?

Question 28mediummultiple choice

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Which of the following describes the purpose of 'gradient accumulation' in fine-tuning scenarios?

Question 29hardmultiple choice

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Refer to the exhibit. Which adjustment is the most immediate and effective way to resolve this OOM error while maintaining the same training architecture?

Exhibit

Error: CUDA out of memory. Tried to allocate 512.00 MiB. GPU 0 has 23.90 GiB total capacity. Memory used at last allocation: 23.50 GiB.

Question 30easymultiple choice

Read the full Fine-Tuning explanation →

Which component in the NVIDIA NeMo framework is specifically designed to manage the configuration and orchestration of large-scale fine-tuning jobs?

Question 31mediummultiple choice

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Why is it important to use a 'warm-up' period in the learning rate schedule when starting a fine-tuning job?

Question 32hardmultiple choice

Read the full Fine-Tuning explanation →

When fine-tuning on a small, domain-specific dataset, why might adding synthetic data generated by a larger model be beneficial?

Question 33mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a Llama 2 7B model with NVIDIA NeMo Framework on a single A100 80GB GPU. They observe that training loss decreases initially but then diverges, and the model outputs become repetitive and incoherent. The team used a learning rate of 5e-5 with AdamW and no warm-up. Which change is most likely to stabilize training and improve convergence?

Question 34mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter Llama model with NVIDIA NeMo on a node of eight A100 80GB GPUs. They want the optimizer state to be partitioned across data-parallel ranks so that per-GPU memory drops, while keeping the model replicas synchronized. Which distributed strategy should they select in the NeMo training configuration?

Question 35mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter Llama model with NVIDIA NeMo Framework on a single A100 80GB GPU. They apply LoRA adapters to the attention projection layers, but the adapters are producing negligible changes to model behavior even after several epochs, and the loss curve stays flat. They confirm the dataset is clean and the tokenizer is correct. Which LoRA configuration issue is the most likely cause?

Question 36mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning an 8B-parameter LLM with LoRA on a single NVIDIA A100 80GB GPU using NVIDIA NeMo. They observe that training loss decreases, but validation loss starts to rise after epoch 2. They want to keep the same dataset and hyperparameters but mitigate overfitting. Which change is most appropriate?

Question 37hardmultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 70B model with NVIDIA NeMo Framework across 16 H100 GPUs. Training completes successfully, but when the fine-tuned checkpoint is evaluated, outputs are incoherent and repeat tokens. The engineer confirms the loss decreased smoothly during training and the validation dataset was held out correctly. Which issue is the most likely explanation?

Question 38hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer fine-tunes a model on a domain corpus with NVIDIA NeMo and observes that training loss falls steadily while validation loss begins rising after the second epoch. The team must produce the most generalizable checkpoint without changing the dataset. Which action should they take?

Question 39easymultiple choice

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A developer needs to fine-tune a 7B LLM for a customer-support chatbot using NVIDIA NeMo. The dataset contains paired instructions and desired responses. Which data format should be used to prepare the dataset for supervised fine-tuning?

Question 40hardmultiple choice

Read the full Fine-Tuning explanation →

A financial services company is fine-tuning an LLM to answer questions about internal policies. The base model performs well on general text but frequently invents policy numbers and effective dates. The team has a curated dataset of 5,000 question-answer pairs with correct citations. Which fine-tuning approach best addresses the hallucination of policy numbers and dates?

Question 41easymultiple choice

Read the full Fine-Tuning explanation →

A company wants to teach a pretrained LLM to follow a specific output format for customer support replies using supervised fine-tuning on NVIDIA GPUs. Which data preparation approach best matches supervised fine-tuning for instruction following?

Question 42mediummultiple choice

Read the full Fine-Tuning explanation →

A data science team is preparing an instruction fine-tuning dataset in NVIDIA NeMo Framework. They notice that after training, the model performs well on the training instructions but poorly on paraphrased versions of the same instructions. They want to improve generalization without increasing dataset size. Which data preparation change is most appropriate?

Question 43hardmultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 13B LLM with LoRA on 4 NVIDIA A100 GPUs using NVIDIA NeMo. They notice that the effective batch size is very small and gradients are noisy, but increasing the per-GPU micro batch size triggers out-of-memory errors. Which technique should they apply to increase the effective batch size without increasing memory per step?

Question 44easymultiple choice

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A developer is preparing a dataset for instruction fine-tuning of an LLM using NVIDIA NeMo. The raw data consists of customer support transcripts with speaker labels and timestamps. Which preprocessing step is most important before training?

Question 45hardmulti select

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An enterprise is fine-tuning a 34B model with NVIDIA NeMo Framework and observes that the validation loss begins rising after the first epoch while training loss continues to fall. The team wants to reduce this divergence and preserve downstream task quality. (Choose two.)

Question 46hardmultiple choice

Read the full Fine-Tuning explanation →

A team fine-tunes a model with NVIDIA NeMo using a packed sequence dataset and notices that some training samples contain several short conversations concatenated. They must ensure the loss is computed only on assistant responses and not on the packed boundaries. Which configuration detail should they verify?

Question 47mediummultiple choice

Read the full Fine-Tuning explanation →

A company wants to fine-tune a 70B LLM to follow domain-specific instructions. The base model already performs well on general language tasks. They have limited labeled data and limited GPU memory. Which approach is most appropriate?

Question 48mediummultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 7B-parameter model with LoRA on a single NVIDIA A100 40GB GPU. The training script reports that the adapter weights are not updating after several hundred steps, and the loss remains flat. The base model weights are frozen as intended. Which LoRA configuration issue is the most likely cause?

Question 49easymultiple choice

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A team is preparing a supervised fine-tuning job in NVIDIA NeMo Framework for a customer-support assistant. They have a large corpus of raw support chat logs with no labels. They want the model to learn to answer customer questions in the company's tone and format. Which data preparation step is most appropriate before training?

Question 50hardmulti select

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An enterprise team is preparing a supervised fine-tuning job in NVIDIA NeMo for a 20B LLM. They want to reduce GPU memory consumption during training without changing the model architecture or the dataset. Which two configuration changes should they apply? (Choose two.)

Question 51hardmulti select

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A team is fine-tuning a 70B-parameter LLM with NVIDIA NeMo on a multi-node cluster and wants to reduce the memory footprint per GPU without changing the model architecture. They are already using mixed precision and a reasonable micro-batch size. Which two techniques should they apply? (Choose two.)

Question 52mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a Llama 3 8B model with NVIDIA NeMo on a single A100 80GB GPU. They observe that validation loss starts to rise while training loss continues to decrease after epoch 2. They want to keep the best generalizing checkpoint without changing the dataset. Which NeMo training configuration strategy should they apply?

Question 53hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer is fine-tuning a 13B parameter model with NVIDIA NeMo using tensor parallelism across four GPUs. After resuming from a checkpoint, training loss spikes and then diverges. The checkpoint was saved with a different tensor parallel size than the current run. What is the most likely cause of the divergence?

Question 54mediummulti select

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A team is fine-tuning a 70B parameter model with NVIDIA NeMo using LoRA on eight H100 GPUs. They want to reduce GPU memory usage during training while preserving the base model's pretrained knowledge. Which two configuration changes should they apply? (Choose two.)

Question 55easymultiple choice

Read the full Fine-Tuning explanation →

A developer is preparing a supervised fine-tuning dataset for an instruction-tuned LLM using NVIDIA NeMo. The dataset contains prompts and responses, but the model sometimes learns to generate the prompt text as part of the response. Which dataset formatting practice should be applied to prevent this?

Question 56hardmultiple choice

Read the full Fine-Tuning explanation →

A research team is fine-tuning a model with NVIDIA NeMo and wants to reduce the risk of catastrophic forgetting of general capabilities while still adapting to a specialized domain. They have a small domain dataset and limited compute. Which fine-tuning approach best balances domain adaptation with retention of pretrained knowledge?

Question 1hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. Which adjustment should you make to the configuration if the model shows signs of overfitting on a small, domain-specific dataset?

Exhibit

Config: { "lora_r": 64, "lora_alpha": 16, "target_modules": ["q_proj", "v_proj"], "task_type": "CAUSAL_LM" }

Question 2mediummultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. Which strategy is most effective to resolve the OOM error while maintaining the 70B parameter model's performance?

Exhibit

Error Log: RuntimeError: CUDA error: out of memory. GPU has 24GB. Model: 70B parameter. Optimizer: AdamW (32-bit).

Question 3mediummulti select

Read the full Fine-Tuning explanation →

Which TWO metrics are most appropriate for evaluating the quality of a fine-tuned model for a creative writing task? (Choose two)

Question 4hardmultiple choice

Read the full Fine-Tuning explanation →

Which TWO of the following strategies are most effective for preventing catastrophic forgetting when fine-tuning a model for a new specialized domain?

Question 5mediummultiple choice

Read the full Fine-Tuning explanation →

When preparing a dataset for supervised fine-tuning (SFT), which THREE practices ensure high-quality model outcomes?

Question 6mediummultiple choice

Read the full network assurance explanation →

An enterprise AI engineer is preparing to fine-tune a large language model using NVIDIA NeMo. The training dataset contains highly confidential internal telemetry logs that must not be exposed to the underlying foundation model weights permanently or leaked during evaluation. Which specialized fine-tuning technique should be selected to minimize parameter modification while preserving core base model weights?

Question 7mediummultiple choice

Read the full Fine-Tuning explanation →

An enterprise AI team is preparing to fine-tune a large language model using NVIDIA NeMo. They want to ensure maximum training stability and avoid vanishing gradients when scaling up to multi-node clusters. Which activation checkpointing strategy should be configured?

Question 8hardmulti select

Read the full Fine-Tuning explanation →

When configuring Parameter-Efficient Fine-Tuning (PEFT) using LoRA within the NVIDIA NeMo framework, which TWO parameters are critical to adjust for balancing adaptation capacity and GPU memory consumption?

Question 9hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer is fine-tuning a 13B parameter model using NVIDIA NeMo Framework with tensor parallelism (TP) of 4 and pipeline parallelism (PP) of 2 on 8 A100 GPUs. They want to maximize throughput while maintaining convergence. They notice that increasing the micro-batch size beyond 4 causes an out-of-memory error, but GPU utilization remains low. Which configuration change should they make to improve throughput without increasing memory usage?

Question 10mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter LLM with NVIDIA NeMo on a node of eight A100 80GB GPUs. They want to maximize training throughput while keeping the global batch size fixed and avoiding GPU memory errors. Which NeMo configuration change is most appropriate?

Question 11mediummulti select

Read the full Fine-Tuning explanation →

A team is adapting a 70B model with LoRA inside NVIDIA NeMo and wants the adapter to learn task-specific behavior without disturbing the frozen base weights. Which two configuration choices support this goal? (Choose two.)

Question 12mediummultiple choice

Read the full Fine-Tuning explanation →

When fine-tuning a Large Language Model using Low-Rank Adaptation (LoRA), which architectural component is primarily modified to reduce computational overhead while maintaining performance?

Question 13mediummultiple choice

Read the full Fine-Tuning explanation →

When using QLoRA for fine-tuning, what is the primary purpose of using the 4-bit NormalFloat (NF4) data type?

Question 14hardmulti select

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Which TWO of the following practices are considered standard procedures for preparing a dataset for Instruction Fine-Tuning (IFT)? (Choose two)

Question 15mediummultiple choice

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What is the primary risk of 'catastrophic forgetting' during the fine-tuning process?

Question 16hardmulti select

Read the full Fine-Tuning explanation →

Which THREE factors significantly influence the memory consumption during LLM fine-tuning? (Choose three)

Question 17easymultiple choice

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What is the primary function of the 'rank' parameter in LoRA?

Question 18mediummultiple choice

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Why is gradient checkpointing useful when fine-tuning a model on a single GPU?

Question 19hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. What is the effective batch size for this fine-tuning job?

Exhibit

Config: { "gradient_accumulation_steps": 16, "per_device_train_batch_size": 1 }

Question 20mediummultiple choice

Read the full Fine-Tuning explanation →

Which of the following describes the purpose of a 'System Prompt' in Instruction Fine-Tuning?

Question 21mediummultiple choice

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What is the primary role of the 'Learning Rate Scheduler' during LLM fine-tuning?

Question 22hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. In the context of a distributed multi-GPU fine-tuning job, what is the most likely cause of this error?

Exhibit

Error Log: [NCCL WARN] Net : Connection refused. Check network configuration.

Question 23mediummultiple choice

Read the full Fine-Tuning explanation →

An enterprise is fine-tuning a large language model using NVIDIA NeMo Framework and encounters GPU out-of-memory errors during the backward pass. The training configuration already uses mixed-precision training (FP16). Which architectural intervention should be applied to resolve memory pressure while retaining the optimizer state precision?

Question 24mediummultiple choice

Read the full Fine-Tuning explanation →

When fine-tuning a base LLM using Parameter-Efficient Fine-Tuning (PEFT) on an NVIDIA H100 GPU, what is the primary advantage of utilizing LoRA compared to full fine-tuning?

Question 25hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. An engineer observes that a model fine-tuned with this LoRA configuration is failing to converge on a highly complex legal document domain. What is the most likely cause of this issue?

Exhibit

config: { "rank": 8, "alpha": 16, "target_modules": ["query_key_value", "dense"], "dropout": 0.05, "bias": "none" }

Question 26mediummultiple choice

Read the full Fine-Tuning explanation →

In the context of NVIDIA NeMo, why is it recommended to use FP8 precision during the fine-tuning process on H100 GPUs?

Question 27mediummultiple choice

Read the full Fine-Tuning explanation →

Which metric is the most reliable indicator that an LLM is overfitting during the fine-tuning phase?

Question 28mediummultiple choice

Read the full Fine-Tuning explanation →

Which of the following describes the purpose of 'gradient accumulation' in fine-tuning scenarios?

Question 29hardmultiple choice

Read the full Fine-Tuning explanation →

Refer to the exhibit. Which adjustment is the most immediate and effective way to resolve this OOM error while maintaining the same training architecture?

Exhibit

Error: CUDA out of memory. Tried to allocate 512.00 MiB. GPU 0 has 23.90 GiB total capacity. Memory used at last allocation: 23.50 GiB.

Question 30easymultiple choice

Read the full Fine-Tuning explanation →

Which component in the NVIDIA NeMo framework is specifically designed to manage the configuration and orchestration of large-scale fine-tuning jobs?

Question 31mediummultiple choice

Read the full Fine-Tuning explanation →

Why is it important to use a 'warm-up' period in the learning rate schedule when starting a fine-tuning job?

Question 32hardmultiple choice

Read the full Fine-Tuning explanation →

When fine-tuning on a small, domain-specific dataset, why might adding synthetic data generated by a larger model be beneficial?

Question 33mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a Llama 2 7B model with NVIDIA NeMo Framework on a single A100 80GB GPU. They observe that training loss decreases initially but then diverges, and the model outputs become repetitive and incoherent. The team used a learning rate of 5e-5 with AdamW and no warm-up. Which change is most likely to stabilize training and improve convergence?

Question 34mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter Llama model with NVIDIA NeMo on a node of eight A100 80GB GPUs. They want the optimizer state to be partitioned across data-parallel ranks so that per-GPU memory drops, while keeping the model replicas synchronized. Which distributed strategy should they select in the NeMo training configuration?

Question 35mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a 13B-parameter Llama model with NVIDIA NeMo Framework on a single A100 80GB GPU. They apply LoRA adapters to the attention projection layers, but the adapters are producing negligible changes to model behavior even after several epochs, and the loss curve stays flat. They confirm the dataset is clean and the tokenizer is correct. Which LoRA configuration issue is the most likely cause?

Question 36mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning an 8B-parameter LLM with LoRA on a single NVIDIA A100 80GB GPU using NVIDIA NeMo. They observe that training loss decreases, but validation loss starts to rise after epoch 2. They want to keep the same dataset and hyperparameters but mitigate overfitting. Which change is most appropriate?

Question 37hardmultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 70B model with NVIDIA NeMo Framework across 16 H100 GPUs. Training completes successfully, but when the fine-tuned checkpoint is evaluated, outputs are incoherent and repeat tokens. The engineer confirms the loss decreased smoothly during training and the validation dataset was held out correctly. Which issue is the most likely explanation?

Question 38hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer fine-tunes a model on a domain corpus with NVIDIA NeMo and observes that training loss falls steadily while validation loss begins rising after the second epoch. The team must produce the most generalizable checkpoint without changing the dataset. Which action should they take?

Question 39easymultiple choice

Read the full Fine-Tuning explanation →

A developer needs to fine-tune a 7B LLM for a customer-support chatbot using NVIDIA NeMo. The dataset contains paired instructions and desired responses. Which data format should be used to prepare the dataset for supervised fine-tuning?

Question 40hardmultiple choice

Read the full Fine-Tuning explanation →

A financial services company is fine-tuning an LLM to answer questions about internal policies. The base model performs well on general text but frequently invents policy numbers and effective dates. The team has a curated dataset of 5,000 question-answer pairs with correct citations. Which fine-tuning approach best addresses the hallucination of policy numbers and dates?

Question 41easymultiple choice

Read the full Fine-Tuning explanation →

A company wants to teach a pretrained LLM to follow a specific output format for customer support replies using supervised fine-tuning on NVIDIA GPUs. Which data preparation approach best matches supervised fine-tuning for instruction following?

Question 42mediummultiple choice

Read the full Fine-Tuning explanation →

A data science team is preparing an instruction fine-tuning dataset in NVIDIA NeMo Framework. They notice that after training, the model performs well on the training instructions but poorly on paraphrased versions of the same instructions. They want to improve generalization without increasing dataset size. Which data preparation change is most appropriate?

Question 43hardmultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 13B LLM with LoRA on 4 NVIDIA A100 GPUs using NVIDIA NeMo. They notice that the effective batch size is very small and gradients are noisy, but increasing the per-GPU micro batch size triggers out-of-memory errors. Which technique should they apply to increase the effective batch size without increasing memory per step?

Question 44easymultiple choice

Read the full Fine-Tuning explanation →

A developer is preparing a dataset for instruction fine-tuning of an LLM using NVIDIA NeMo. The raw data consists of customer support transcripts with speaker labels and timestamps. Which preprocessing step is most important before training?

Question 45hardmulti select

Read the full Fine-Tuning explanation →

An enterprise is fine-tuning a 34B model with NVIDIA NeMo Framework and observes that the validation loss begins rising after the first epoch while training loss continues to fall. The team wants to reduce this divergence and preserve downstream task quality. (Choose two.)

Question 46hardmultiple choice

Read the full Fine-Tuning explanation →

A team fine-tunes a model with NVIDIA NeMo using a packed sequence dataset and notices that some training samples contain several short conversations concatenated. They must ensure the loss is computed only on assistant responses and not on the packed boundaries. Which configuration detail should they verify?

Question 47mediummultiple choice

Read the full Fine-Tuning explanation →

A company wants to fine-tune a 70B LLM to follow domain-specific instructions. The base model already performs well on general language tasks. They have limited labeled data and limited GPU memory. Which approach is most appropriate?

Question 48mediummultiple choice

Read the full Fine-Tuning explanation →

An ML engineer is fine-tuning a 7B-parameter model with LoRA on a single NVIDIA A100 40GB GPU. The training script reports that the adapter weights are not updating after several hundred steps, and the loss remains flat. The base model weights are frozen as intended. Which LoRA configuration issue is the most likely cause?

Question 49easymultiple choice

Read the full Fine-Tuning explanation →

A team is preparing a supervised fine-tuning job in NVIDIA NeMo Framework for a customer-support assistant. They have a large corpus of raw support chat logs with no labels. They want the model to learn to answer customer questions in the company's tone and format. Which data preparation step is most appropriate before training?

Question 50hardmulti select

Read the full Fine-Tuning explanation →

An enterprise team is preparing a supervised fine-tuning job in NVIDIA NeMo for a 20B LLM. They want to reduce GPU memory consumption during training without changing the model architecture or the dataset. Which two configuration changes should they apply? (Choose two.)

Question 51hardmulti select

Read the full Fine-Tuning explanation →

A team is fine-tuning a 70B-parameter LLM with NVIDIA NeMo on a multi-node cluster and wants to reduce the memory footprint per GPU without changing the model architecture. They are already using mixed precision and a reasonable micro-batch size. Which two techniques should they apply? (Choose two.)

Question 52mediummultiple choice

Read the full Fine-Tuning explanation →

A team is fine-tuning a Llama 3 8B model with NVIDIA NeMo on a single A100 80GB GPU. They observe that validation loss starts to rise while training loss continues to decrease after epoch 2. They want to keep the best generalizing checkpoint without changing the dataset. Which NeMo training configuration strategy should they apply?

Question 53hardmultiple choice

Read the full Fine-Tuning explanation →

An engineer is fine-tuning a 13B parameter model with NVIDIA NeMo using tensor parallelism across four GPUs. After resuming from a checkpoint, training loss spikes and then diverges. The checkpoint was saved with a different tensor parallel size than the current run. What is the most likely cause of the divergence?

Question 54mediummulti select

Read the full Fine-Tuning explanation →

A team is fine-tuning a 70B parameter model with NVIDIA NeMo using LoRA on eight H100 GPUs. They want to reduce GPU memory usage during training while preserving the base model's pretrained knowledge. Which two configuration changes should they apply? (Choose two.)

Question 55easymultiple choice

Read the full Fine-Tuning explanation →

A developer is preparing a supervised fine-tuning dataset for an instruction-tuned LLM using NVIDIA NeMo. The dataset contains prompts and responses, but the model sometimes learns to generate the prompt text as part of the response. Which dataset formatting practice should be applied to prevent this?

Question 56hardmultiple choice

Read the full Fine-Tuning explanation →

A research team is fine-tuning a model with NVIDIA NeMo and wants to reduce the risk of catastrophic forgetting of general capabilities while still adapting to a specialized domain. They have a small domain dataset and limited compute. Which fine-tuning approach best balances domain adaptation with retention of pretrained knowledge?