NCA-GENL Core Machine Learning and AI Knowledge — All Questions With Answers

Question 1mediummultiple choice

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Which TWO of the following statements regarding Gradient Descent variants are correct?

Question 2easymultiple choice

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Which of the following activation functions is most commonly used in modern transformer-based LLMs to avoid the vanishing gradient problem?

Question 3hardmultiple choice

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Which THREE of the following are primary benefits of using Mixed Precision Training on NVIDIA GPUs?

Question 4hardmultiple choice

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Which TWO of the following are true regarding the transformer's Multi-Head Attention (MHA) mechanism?

Question 5mediummultiple choice

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A machine learning engineer is training a large transformer model on a multi-GPU cluster. The training process periodically hangs due to gradient synchronization delays during backpropagation. Which technique should be implemented to mitigate this bottleneck?

Question 6mediummultiple choice

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Refer to the exhibit. An engineer is deploying a PyTorch DistributedDataParallel job on an NVIDIA DGX cluster. Based on the logs, what is the root cause of the training failure?

Exhibit

2023-10-27 10:00:00 [ERROR] DistributedDataParallel: detected signal: SIGTERM. 2023-10-27 10:00:05 [INFO] Rank 0: Expected 8 GPUs, but found 7 nodes with 1 GPU each. 2023-10-27 10:00:10 [WARN] NCCL error: unhandled system error.

Question 7hardmultiple choice

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When designing a high-performance inference pipeline using NVIDIA TensorRT, which TWO practices are essential for maximizing throughput and reducing latency on A100 GPUs?

Question 8mediummultiple choice

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When preparing a dataset for training large-scale foundation models, which THREE data-cleaning steps are recommended to ensure high model quality?

Question 9hardmultiple choice

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Which THREE of the following are primary benefits of utilizing NVIDIA NeMo for LLM development?

Question 10hardmultiple choice

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Which TWO of the following statements correctly describe the role of gradient descent in training modern deep learning models?

Question 11mediummultiple choice

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Which term describes the process of fine-tuning a model on a smaller, task-specific dataset after it has been pre-trained on a large, general-purpose dataset?

Question 12mediummultiple choice

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A machine learning engineer is training a large language model and observes the training loss plateauing prematurely. Which technique is most appropriate to address this issue while maintaining computational efficiency?

Question 13mediummulti select

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Which TWO of the following are potential issues when training deep learning models on highly imbalanced datasets?

Question 14easymultiple choice

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A data scientist is building a text classifier and wants to evaluate how well the trained model will perform on completely unseen data. They have a dataset of 10,000 labeled examples. Which evaluation approach provides the most reliable estimate of the model's generalization performance?

Question 15hardmulti select

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A research team is pretraining a transformer-based language model on a large corpus. They want to ensure the model learns bidirectional context, unlike traditional left-to-right models. Which TWO pretraining objectives are designed to achieve this? (Choose two.)

Question 16hardmulti select

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A research team is pre-training a large language model on a massive corpus of text. They want to ensure that the model learns useful representations and generalizes well to downstream tasks. Which TWO of the following techniques are commonly used during pre-training to improve the model's ability to capture contextual relationships and avoid overfitting? (Choose two.)

Question 17hardmultiple choice

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A researcher is training a transformer-based language model and wants to prevent the model from attending to future tokens during training. Which mechanism should be implemented in the self-attention layer?

Question 18mediummulti select

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A team is deploying a large language model for real-time inference on NVIDIA GPUs. They want to reduce latency and increase throughput. Which TWO techniques are most effective for achieving these goals? (Choose two.)

Question 19mediummulti select

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A team is deploying a large language model for real-time chatbot inference on NVIDIA GPUs. They need to reduce latency without sacrificing output quality. Which two techniques should they implement? (Choose two.)

Question 20mediummultiple choice

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A researcher is training a large language model and notices the training loss plateaus early while validation loss increases. What is the most likely cause, and which action should be taken?

Question 21mediummultiple choice

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In the context of transformer models, what is the purpose of the 'Attention Mask' during the training process?

Question 22mediummultiple choice

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What is the primary function of Layer Normalization in a transformer architecture?

Question 23mediummultiple choice

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Why is 'Warmup' used for the learning rate schedule during the initial phase of training large language models?

Question 24hardmultiple choice

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What is the primary motivation for using Position Embeddings in a transformer model?

Question 25mediummultiple choice

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Which THREE techniques are commonly used to improve the efficiency of inference for large language models?

Question 26easymultiple choice

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What is the role of 'Temperature' in the context of LLM text generation?

Question 27easymultiple choice

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Which component of an NVIDIA AI stack is primarily responsible for providing a low-level API for high-performance collective communication primitives across multi-GPU nodes?

Question 28mediummultiple choice

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A researcher is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) techniques. Which method is specifically designed to inject trainable low-rank matrices into the transformer layers to reduce the number of trainable parameters?

Question 29mediummultiple choice

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Refer to the exhibit. The configuration shows the use of FSDP with mixed precision. What is the main benefit of using 'bf16' (Bfloat16) over 'fp16' in this context?

Exhibit

config.yaml: 
optimizer: adamw
precision: bf16
sharding: fsdp
fsdp_config:
  backward_prefetch: backward_pre
  mixed_precision: true

Question 30mediummultiple choice

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When evaluating an LLM for factual accuracy, which metric is most effective at detecting hallucinations compared to simple word-overlap metrics?

Question 31hardmultiple choice

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In the context of NVIDIA's Tensor Core architecture, what is the primary purpose of 'Sparsity' support?

Question 32mediummultiple choice

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When implementing Retrieval-Augmented Generation (RAG), why is the choice of 'Chunk Size' critical for model retrieval performance?

Question 33mediummultiple choice

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Refer to the exhibit. This error occurs during the training of an LLM. What is the most likely cause for this 'device-side assert' error?

Exhibit

Error: CUDA error: device-side assert triggered. Traceback: ... in forward_pass ... loss = criterion(logits, targets) ...

Question 34easymultiple choice

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Which of the following describes the 'Warm-up' phase in the context of training deep neural networks?

Question 35hardmultiple choice

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When utilizing Pipeline Parallelism (PP) in LLM training, what is the 'pipeline bubble' and how is it minimized?

Question 36mediummultiple choice

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A data scientist is training a transformer model and observes that the training loss is decreasing while the validation loss is increasing. Which technique should be prioritized to address this specific generalization challenge?

Question 37mediummultiple choice

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Refer to the exhibit. Which technique is most appropriate to prevent this specific training failure?

Exhibit

2023-10-27 10:00:01 [INFO] Training Epoch 5/10 completed
2023-10-27 10:00:05 [WARNING] Gradient norm 452.1 exceeds threshold 1.0
2023-10-27 10:00:06 [ERROR] Divergence detected: Loss is NaN

Question 38easymultiple choice

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Which machine learning paradigm involves an agent learning to make decisions by performing actions in an environment to maximize a cumulative reward?

Question 39mediummultiple choice

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In the context of Large Language Models, what is the primary purpose of 'Attention mechanisms' as introduced in the Transformer architecture?

Question 40mediummultiple choice

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Which THREE of the following are valid methods for improving the inference performance of a deployed deep learning model?

Question 41hardmultiple choice

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In the context of generative AI, what is the 'mode collapse' problem in GANs, and why is it a significant challenge?

Question 42hardmultiple choice

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Refer to the exhibit. Which hyperparameter configuration in the provided JSON is directly responsible for preventing overfitting through weight penalty?

Exhibit

{
  "policy_name": "model_access_control",
  "enable_gradient_check": true,
  "optimizer_type": "adam",
  "learning_rate_scheduler": "cosine",
  "weight_decay": 0.05,
  "dropout_rate": 0.1
}

Question 43easymultiple choice

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Which of the following activation functions is most commonly used in hidden layers of deep neural networks to mitigate the vanishing gradient problem?

Question 44mediummultiple choice

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What is the primary function of the 'Softmax' layer at the output of a multi-class classification model?

Question 45easymultiple choice

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Which of the following best defines 'Generalization' in machine learning?

Question 46mediummultiple choice

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When training a model with a very large dataset, which approach provides the best balance between computational efficiency and model convergence?

Question 47mediummultiple choice

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Refer to the exhibit. Which technique is most effective for preventing the reported NaN error during model training?

Exhibit

2023-10-27 10:00:01 INFO: Training iteration 5000/10000
2023-10-27 10:00:05 WARNING: Gradient norm 500.2 exceeds threshold 1.0
2023-10-27 10:00:06 ERROR: Model weights contain NaN values

Question 48easymultiple choice

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Which of the following describes the purpose of a validation set in machine learning?

Question 49mediummultiple choice

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Which activation function is most commonly used in the hidden layers of deep neural networks to mitigate the vanishing gradient problem?

Question 50mediummultiple choice

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Refer to the exhibit. The training loss is oscillating and failing to converge. What is the most likely immediate adjustment needed?

Exhibit

config.json:
{
  "optimizer": "Adam",
  "learning_rate": 0.01,
  "batch_size": 32,
  "precision": "FP32"
}

training_log:
Epoch 1: loss = 2.45
Epoch 2: loss = 2.38
Epoch 3: loss = 2.42
Epoch 4: loss = 2.39

Question 51hardmultiple choice

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Which component of an NVIDIA Transformer Engine is specifically designed to accelerate training on supported GPUs by dynamically adjusting precision?

Question 52mediummultiple choice

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What is the primary function of the 'Attention' mechanism in Transformer models?

Question 53mediummultiple choice

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Refer to the exhibit. Which strategy is most effective for resolving this memory error without changing the hardware?

Exhibit

Error: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 24.00 GiB total capacity; 21.50 GiB already allocated)

Question 54hardmulti select

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Which THREE of the following factors are critical when choosing a foundation model for an enterprise generative AI application?

Question 55mediummultiple choice

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What is the primary role of 'Loss Scaling' when training deep learning models in FP16 precision?

Question 56mediummultiple choice

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An enterprise machine learning team is training a large-scale transformer model on a cluster of NVIDIA A100 GPUs using mixed precision (FP16). During the initial training phase, the team notices sudden numerical underflow resulting in vanishing gradients and stalled loss convergence. Which optimization technique must be applied to mitigate this issue without sacrificing the memory-efficiency benefits of FP16?

Question 57mediummultiple choice

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An AI researcher is fine-tuning a large language model and wants to minimize GPU memory consumption during training without altering the model's primary weight representations or introducing quantization error during inference. Which technique provides this capability by decomposing weight matrices into low-rank trainable adaptation matrices?

Question 58hardmultiple choice

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Refer to the exhibit. A machine learning engineer reviews the monitoring output from a two-GPU distributed training job running on an NVIDIA DGX system. GPU 0 shows low utilization despite high memory consumption, while GPU 1 shows high utilization and high memory consumption. What is the most likely root cause of this performance imbalance?

Question 59hardmultiple choice

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An ML engineer is deploying a Transformer-based inference service on an NVIDIA TensorRT-LLM runtime. To maximize inference throughput and reduce latency under heavy concurrent user traffic, the engineer needs to select the optimal decoding batching strategy. Which technique allows multiple incoming dynamic sequence requests to be batched together at the token level rather than waiting for entire sequences to complete?

Question 60easymultiple choice

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A data scientist is preparing a transformer-based language model for a text summarization task. She notices that the input sequences in her dataset vary widely in length, from a few tokens to several thousand. She decides to set a fixed maximum sequence length and pad shorter sequences with a special token. Which component of the transformer architecture is primarily responsible for handling the positional information of tokens in these sequences?

Question 61easymultiple choice

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A data scientist is preprocessing a text corpus to train a large language model. They want to convert each word into a dense vector representation that captures semantic relationships before feeding it into the transformer. Which technique should they use?

Question 62mediummultiple choice

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A machine learning engineer is training a large language model and notices that the model performs exceptionally well on the training data but poorly on a held-out test set. Which technique is most appropriate to mitigate this issue?

Question 63mediummultiple choice

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A machine learning engineer is training a large language model on a cluster of NVIDIA GPUs. During training, she observes that the loss occasionally spikes to NaN, causing the training to fail. She suspects that the issue is related to the numerical precision of the computations. Which technique is most appropriate to mitigate this issue while maintaining training stability?

Question 64easymultiple choice

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A data scientist is preparing a labeled dataset of 50,000 customer support tickets for supervised fine-tuning of an LLM. Each ticket must be assigned exactly one of eight department labels. Which loss function is most appropriate for training this classification head?

Question 65mediummulti select

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A machine learning engineer is training a deep neural network and notices that the training loss decreases but the validation loss starts to increase after several epochs. Which two techniques are most appropriate to mitigate this issue? (Choose two.)

Question 66mediummultiple choice

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A team is deploying a large language model for real-time text generation. They observe that the model sometimes produces repetitive and dull outputs, especially when generating longer sequences. They want to encourage more diverse and creative text without significantly degrading coherence. Which decoding strategy should they consider?

Question 67easymultiple choice

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A data science team is building a model to predict whether a customer will churn based on historical account activity. They have a large dataset with labeled outcomes (churned or not churned). Which type of machine learning is most appropriate for this task?

Question 68mediummultiple choice

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An ML engineer trains a sentiment classifier on 10,000 movie reviews but only 300 are negative. The model predicts positive for nearly every review, including obvious negative ones. Which technique best addresses this class imbalance during training?

Question 69mediummultiple choice

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A machine learning engineer is training a deep neural network for image classification. They notice that the training loss decreases steadily, but the validation loss starts to increase after a few epochs. Which technique is most directly aimed at addressing this issue?

Question 70easymultiple choice

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A data science team at a retail company is building a neural network to predict customer churn from tabular data with mixed numerical and categorical features. They want the model to output a probability between 0 and 1, and they are training with a standard gradient descent optimizer. Which loss function is most appropriate for this binary classification task?

Question 71mediummultiple choice

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A team is deploying a large language model for real-time text generation and notices that inference latency is too high. They want to reduce latency without retraining the model. Which technique is most appropriate?

Question 72mediummultiple choice

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A team is pretraining a large language model on a cluster of NVIDIA GPUs. They observe that the model's training loss decreases steadily for the first few epochs but then suddenly spikes and eventually becomes NaN. They suspect this is due to exploding gradients. Which technique is most appropriate to address this issue?

Question 73mediummultiple choice

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A team trains a transformer language model on a large corpus but the model achieves very low training loss while performing poorly on held-out text. Which action most directly addresses this outcome?

Question 74mediummultiple choice

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A machine learning engineer is training a convolutional neural network for image classification and notices that the training loss decreases steadily, but the validation loss starts increasing after a few epochs. The training set is large and representative. Which technique is most directly aimed at addressing this phenomenon?

Question 75mediummulti select

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A team is preparing a dataset to train a generative AI model for text summarization. They want to ensure the model generalizes well and does not simply memorize the training examples. Which TWO practices should they follow? (Choose two.)

Question 76hardmultiple choice

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A data scientist is fine-tuning a pretrained large language model on a small domain-specific dataset. The model achieves high accuracy on the training set but poor performance on a held-out validation set. Which technique is most likely to improve the model's generalization?

Question 77mediummultiple choice

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A data scientist is pretraining a 12-layer transformer encoder on a corpus of legal contracts. To prevent the model from simply copying each token to its output during masked language modeling, the team needs a strategy that forces the model to learn bidirectional context. Which masking approach should they apply?

Question 78mediummultiple choice

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A machine learning engineer is evaluating a generative language model for a chatbot application. They notice that the model frequently generates repetitive phrases and gets stuck in loops. Which decoding strategy is most likely to reduce this repetition?

Question 79hardmultiple choice

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A research team is pretraining a transformer on a corpus of 200 billion tokens. They want the model to learn bidirectional context so each token attends to both left and right neighbors during pretraining. Which pretraining objective fits this requirement?

Question 80hardmultiple choice

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A machine learning engineer is evaluating a large language model (LLM) on a text generation task. They observe that the model produces coherent and fluent sentences, but the content is factually incorrect and sometimes contradicts known facts. Which term best describes this phenomenon?

Question 81mediummultiple choice

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A developer is using a pretrained large language model for a text summarization task. They want to adapt the model to a domain-specific corpus of legal documents but have limited GPU memory and a small labeled dataset. Which fine-tuning approach is most parameter-efficient and suitable for this scenario?

Question 82mediummultiple choice

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A machine learning engineer is preprocessing a dataset for a generative AI model and wants to ensure that the input features have a similar scale. Which technique is most appropriate?

Question 83hardmultiple choice

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An ML engineer is training a transformer-based language model on a single NVIDIA A100 GPU. They observe that the training loss decreases initially but then becomes NaN after a few hundred steps. The learning rate is 1e-4, and mixed precision with FP16 is enabled. Which action is most likely to stabilize training while preserving the benefits of mixed precision?

Question 84hardmultiple choice

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A team is deploying a large language model for real-time inference on an NVIDIA GPU. They observe that the first few inference requests have high latency, but subsequent requests are much faster. What is the most likely explanation for this behavior?

Question 85hardmultiple choice

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A machine learning engineer is fine-tuning a pre-trained language model on a small domain-specific dataset. She notices that the model quickly achieves high accuracy on the training set but performs poorly on the validation set. She wants to mitigate this overfitting without collecting more data. Which technique is most appropriate?

Question 86hardmulti select

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A team deploys a retrieval-augmented generation pipeline and observes that answers frequently cite facts not present in the retrieved passages. They want to reduce this unsupported generation behavior. (Choose two.)

Question 87easymultiple choice

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A developer is building a text summarization assistant that must produce concise, faithful summaries of long support tickets. The team wants to fine-tune a pre-trained large language model on a small labeled dataset of ticket-summary pairs. Which training approach best matches this goal?

Question 88mediummultiple choice

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A data scientist is working with a dataset that has a highly skewed distribution, with one class representing only 2% of the samples. They are training a binary classifier and notice that the model predicts the majority class almost exclusively. Which technique is most appropriate to address this issue?

Question 89easymultiple choice

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A data scientist is preparing a dataset of 50,000 customer support chat transcripts to fine-tune an LLM for a helpdesk assistant. The raw text contains HTML tags, inconsistent whitespace, and occasional personal information such as email addresses. Which preprocessing step should be performed FIRST to prepare the text for tokenization?

Question 90hardmulti select

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A team is evaluating a generative large language model for a customer support chatbot. They need to ensure the model produces factually accurate and contextually appropriate responses while avoiding harmful or biased outputs. Which two techniques are most effective for aligning the model's behavior with these safety and quality requirements? (Choose two.)

Question 91mediummulti select

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A machine learning engineer is evaluating a fine-tuned LLM for a customer-facing summarization task. The model produces fluent summaries, but the team needs to detect when the model generates content that is not supported by the source document. Which TWO evaluation approaches are appropriate for measuring factual consistency between the generated summary and the source? (Choose two.)

Question 92mediummultiple choice

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A data scientist is preparing a dataset of 50,000 customer support conversations to fine-tune a large language model. The conversations vary widely in length, and many exceed the model's maximum context window. The team wants to preserve conversational coherence while avoiding truncation that removes critical resolution details. Which preprocessing strategy is most appropriate?

Question 93hardmultiple choice

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A machine learning engineer is deploying a transformer-based language model for real-time translation. They observe that inference latency is too high for the required throughput. The model uses standard multi-head self-attention. Which modification is most likely to reduce latency without significantly degrading translation quality?

Question 94hardmultiple choice

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A team is pre-training a 7-billion-parameter LLM on a large text corpus. They observe that the training loss decreases steadily but the validation loss begins to increase after a certain number of steps. The training and validation data come from the same distribution, and the model has not yet reached the compute budget. Which action is most appropriate to address this behavior?

Question 95mediummultiple choice

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A machine learning engineer is evaluating a generative LLM for a customer-facing question-answering system. The model produces fluent answers, but during testing it confidently states incorrect facts about company policies. The team wants a metric that specifically measures whether the model's output is supported by the provided source documents. Which evaluation approach is most appropriate?

Question 96easymultiple choice

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A machine learning engineer is evaluating a language model's performance on a text summarization task. The model achieves a BLEU score of 0.45 and a ROUGE-L score of 0.62 on the test set. The engineer wants to understand how well the model captures the overall meaning of the source documents. Which evaluation metric should they prioritize?

Question 97hardmultiple choice

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A researcher is fine-tuning a large language model on a downstream task with a small dataset. They notice that the model achieves high training accuracy but poor validation accuracy. Which regularization technique is most appropriate to address this issue?

Question 98mediummultiple choice

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A developer is building a retrieval-augmented generation (RAG) system for an internal knowledge base. The system must answer questions using company documents that are updated frequently. Which component is primarily responsible for retrieving the most relevant document chunks to include in the LLM's context?

Question 99hardmulti select

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A research team is designing a decoder-only transformer LLM for long-document question answering. They want to reduce the quadratic computational cost of self-attention so that training on sequences of 32,000 tokens is feasible on their GPU cluster. Which two techniques are appropriate for this goal? (Choose two.)

Question 100hardmultiple choice

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A research team is training a transformer-based language model and wants to reduce the computational cost of the self-attention mechanism for very long input sequences. They are considering replacing the standard scaled dot-product attention with an approximation. Which statement accurately describes a trade-off of using an approximate attention method?

Question 101hardmultiple choice

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A financial services company is deploying an LLM-based assistant that must answer questions about internal compliance documents. The documents are updated weekly, and the company cannot retrain the model every week. The assistant must cite the exact source passage for each answer. Which architecture best satisfies these requirements?

2023-10-27 10:00:00 [ERROR] DistributedDataParallel: detected signal: SIGTERM. 2023-10-27 10:00:05 [INFO] Rank 0: Expected 8 GPUs, but found 7 nodes with 1 GPU each. 2023-10-27 10:00:10 [WARN] NCCL error: unhandled system error.

config.json: { "optimizer": "Adam", "learning_rate": 0.01, "batch_size": 32, "precision": "FP32" } training_log: Epoch 1: loss = 2.45 Epoch 2: loss = 2.38 Epoch 3: loss = 2.42 Epoch 4: loss = 2.39