Hugging Face Transformers Course Practice Test
The Hugging Face Transformers Course Practice Test is a comprehensive assessment designed to evaluate your understanding of the Hugging Face ecosystem, from foundational concepts to advanced implementation strategies. This exam covers five core areas: Fundamentals (tokenization, model architectures, and the Transformer paradigm), Core Knowledge (pipeline APIs, model hubs, and fine-tuning workflows), Practical Application (training loops, dataset loading, and inference optimization), Advanced Topics (custom models, quantization, distributed training, and PEFT), and Best Practices (version control with Git LFS, logging, reproducibility, and deployment considerations). The test is ideal for machine learning engineers, data scientists, NLP practitioners, and students who have completed an introductory course on Transformers or have hands-on experience with Hugging Face libraries. By taking this practice test, you will solidify your ability to navigate the Hugging Face ecosystem independently, debug common issues, and apply state-of-the-art NLP models to real-world problems. You will gain confidence in selecting the right model for a task, optimizing inference speed, and managing large-scale training workflows. The 88 questions are structured to mimic real-world decision-making scenarios, ensuring that you not only recall facts but also apply reasoning to practical challenges. This exam serves as both a diagnostic tool and a learning accelerator, helping you identify gaps in your knowledge before tackling production-level projects or advanced certifications.
Preguntas de Muestra
Prueba algunas preguntas para ver cómo es el examen completo.
A practitioner configures TrainingArguments for a three-epoch fine-tune of a DistilBERT sentiment classifier. They want automatic evaluation on the validation split at the end of every epoch and want the Trainer to load the best checkpoint (by validation loss) at the end of training. Which pair of arguments achieves this behavior?
After local fine-tuning a developer executes model.save_pretrained("./my-bert-ner") and tokenizer.save_pretrained("./my-bert-ner"). Which pair of from_pretrained calls will correctly reload both artifacts from the local directory?
A global customer-support team wants one model that can classify sentiment of reviews written in English, French, German, Spanish, and Chinese without training separate monolingual models. Which widely referenced model family from the course and Model Hub was pretrained on 100+ languages with a shared vocabulary and exhibits strong zero-shot cross-lingual transfer after fine-tuning on any single language?
Your compute_metrics function receives an EvalPrediction whose .predictions are logits of shape (n_examples, 2) for a binary task. You write: preds = np.argmax(predictions.predictions, axis=-1); then use accuracy_score(labels, preds). Which subtle bug will appear if your labels are still the original 0/1 integers but the model was trained with a different label mapping?
You evaluate a fine-tuned sequence classifier on the validation set using the Trainer. The returned dictionary contains "eval_loss" and "eval_accuracy". Where did the accuracy value originate?