TOEIC Part 2


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**1.**
**Q:** Did you update the training script this morning?
(A) Yes, I modified the loss function.
(B) It’s a supervised model.
(C) I’ll open the notebook.

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**2.**
**Q:** Where is the latest model checkpoint stored?
(A) About 10% of the data is missing.
(B) In the `/checkpoints/` directory.
(C) Yes, it’s working fine.

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**3.**
**Q:** Who is responsible for hyperparameter tuning?
(A) I think Jun is handling that.
(B) The loss is still too high.
(C) Yes, it uses dropout.

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**4.**
**Q:** When will the accuracy report be ready?
(A) In the preprocessing script.
(B) By 5 p.m. today.
(C) No, we used ReLU.

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**5.**
**Q:** Have they trained the model on GPU?
(A) Yes, it finished in 30 minutes.
(B) It's on the whiteboard.
(C) They annotated the images.

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**6.**
**Q:** What optimizer are you using for this model?
(A) I chose Adam for faster convergence.
(B) We’ll try later.
(C) No, it's a classification task.

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**7.**
**Q:** Is the dataset already cleaned?
(A) Not yet, I’ll handle it after lunch.
(B) Yes, I used a sigmoid function.
(C) No, the layers are frozen.

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**8.**
**Q:** Why did you switch to a transformer model?
(A) Because it handles sequence data better.
(B) It was trained for 20 epochs.
(C) I ran it on batch size 128.

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**9.**
**Q:** Do you need help visualizing the confusion matrix?
(A) No thanks, I already plotted it.
(B) It’s an imbalanced dataset.
(C) The model is converging well.

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**10.**
**Q:** Who designed the CNN architecture?
(A) Brian did it during the weekend.
(B) It's about 3 gigabytes.
(C) Yes, it’s quite accurate.

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**11.**
**Q:** Should we normalize the features before training?
(A) Definitely, it improves stability.
(B) The test loss is lower.
(C) We froze the encoder.

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**12.**
**Q:** What’s causing the validation accuracy to drop?
(A) Possibly overfitting from too many epochs.
(B) I think it’s under the desk.
(C) Yes, we recompiled the model.

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**13.**
**Q:** Did you already merge the CSV files?
(A) Yes, all columns are aligned now.
(B) The activation function is sigmoid.
(C) I filtered by category.

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**14.**
**Q:** Where did you upload the model output?
(A) To the shared cloud folder.
(B) It uses a sequential model.
(C) About five minutes ago.

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**15.**
**Q:** Are the test results consistent with training data?
(A) No, the metrics are quite different.
(B) I labeled 500 samples.
(C) It’s a binary label.

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**16.**
**Q:** Has the training been completed yet?
(A) Yes, it finished overnight.
(B) It's in the loss plot.
(C) The features were extracted.

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**17.**
**Q:** Why didn’t the model generalize well?
(A) Maybe the training set was too small.
(B) It outputs three classes.
(C) I used a pretrained backbone.

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**18.**
**Q:** Can you explain the sharp drop in the learning curve?
(A) Sure, it's due to early stopping.
(B) The image quality was high.
(C) Yes, the tokenization is done.

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**19.**
**Q:** Is the final report ready to submit?
(A) Not yet, I’m revising the figures.
(B) We’re using PyTorch.
(C) I’ll share the notebook.

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**20.**
**Q:** What metric are you using for evaluation?
(A) Precision and recall mainly.
(B) It’s running on GPU.
(C) I downloaded the paper.

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## ✅ 解答一覧（正しい応答選択）

1. (A)
2. (B)
3. (A)
4. (B)
5. (A)
6. (A)
7. (A)
8. (A)
9. (A)
10. (A)
11. (A)
12. (A)
13. (A)
14. (A)
15. (A)
16. (A)
17. (A)
18. (A)
19. (A)
20. (A)

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**21.**
**Q:** Is this the latest version of the dataset?
(A) Yes, it was updated this morning.
(B) No, I trained it on CPU.
(C) A pre-trained model is useful.

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**22.**
**Q:** When will you run the model evaluation?
(A) After finishing the data cleaning.
(B) It’s a regression model.
(C) The loss was decreasing.

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**23.**
**Q:** Who’s maintaining the GPU server now?
(A) I believe it’s Rina.
(B) It runs on Python.
(C) We used a different loss.

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**24.**
**Q:** Did he include dropout in the network?
(A) Yes, on every hidden layer.
(B) It’s an image dataset.
(C) No, it’s for training.

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**25.**
**Q:** Where did you get the test accuracy results?
(A) From the metrics dashboard.
(B) Yes, it uses an API.
(C) The model is fine-tuned.

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**26.**
**Q:** Are we using the same tokenizer as before?
(A) No, I switched to a different one.
(B) The model converged fast.
(C) It's written in NumPy.

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**27.**
**Q:** Has the overfitting issue been resolved?
(A) Yes, after using regularization.
(B) That depends on the input shape.
(C) The activation was softmax.

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**28.**
**Q:** What is the batch size for this training?
(A) I set it to 64.
(B) It predicts labels.
(C) No, not supervised.

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**29.**
**Q:** Did you freeze the encoder layers?
(A) Yes, to avoid retraining them.
(B) The dataset is balanced.
(C) It’s a loss function.

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**30.**
**Q:** Who submitted the model to the leaderboard?
(A) I think Hiroki did.
(B) It’s a vision task.
(C) On the GitHub page.

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**31.**
**Q:** Are we collecting new training data today?
(A) Yes, the sensors are already deployed.
(B) I adjusted the optimizer.
(C) That’s a log-likelihood plot.

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**32.**
**Q:** Why didn’t the F1-score improve?
(A) The labels might be noisy.
(B) We added more layers.
(C) It runs on TensorFlow.

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**33.**
**Q:** When are you going to upload the model weights?
(A) After I compress them.
(B) It’s a validation issue.
(C) The data were normalized.

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**34.**
**Q:** Did the training take longer than expected?
(A) Yes, especially with a large dataset.
(B) We used fewer features.
(C) It’s a multi-class setting.

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**35.**
**Q:** Where is the source code hosted?
(A) On our internal Git repository.
(B) It’s a classification model.
(C) We used scikit-learn.

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**36.**
**Q:** Who gave the presentation on explainable AI?
(A) Dr. Sato from our team.
(B) It requires fine-tuning.
(C) The input is tokenized.

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**37.**
**Q:** Do you want help adjusting the learning rate?
(A) Sure, I’m still trying to find the best value.
(B) We split the dataset.
(C) No, it's a binary label.

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**38.**
**Q:** Has the early stopping been configured?
(A) Yes, with patience set to five.
(B) It’s a pretrained transformer.
(C) No, it’s underfitting.

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**39.**
**Q:** What kind of task is this model trained for?
(A) It’s a text classification task.
(B) No, it’s overfitting.
(C) The loss has plateaued.

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**40.**
**Q:** Are the metrics consistent across all folds?
(A) Yes, the variance is low.
(B) That’s an input shape.
(C) We stored the logs.

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## ✅ 解答一覧（正答のみ）

21. (A)
22. (A)
23. (A)
24. (A)
25. (A)
26. (A)
27. (A)
28. (A)
29. (A)
30. (A)
31. (A)
32. (A)
33. (A)
34. (A)
35. (A)
36. (A)
37. (A)
38. (A)
39. (A)
40. (A)

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**41.**
**Q:** Did the training process finish successfully?
(A) Yes, with no errors in the log.
(B) I think it's convolutional.
(C) It uses cross-entropy.

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**42.**
**Q:** Where can I find the loss plot?
(A) It’s shown on the last cell in the notebook.
(B) The dataset was very clean.
(C) We normalized it yesterday.

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**43.**
**Q:** Has the data pipeline been tested?
(A) Not yet, we’re still debugging.
(B) It was labeled manually.
(C) Yes, we added more epochs.

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**44.**
**Q:** What’s the function of this output layer?
(A) It maps logits to probabilities.
(B) I changed the learning rate.
(C) The RAM was full.

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**45.**
**Q:** When did you annotate the text data?
(A) Over the weekend.
(B) We’ll use a transformer.
(C) Yes, I deployed it.

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**46.**
**Q:** Is this model architecture open source?
(A) Yes, it’s on GitHub under MIT license.
(B) The tokens were removed.
(C) It’s a sequence task.

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**47.**
**Q:** Who created this interactive dashboard?
(A) That was built by Maria.
(B) I filtered by class.
(C) We’ll freeze the encoder.

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**48.**
**Q:** Why are we using dropout here?
(A) To reduce overfitting.
(B) The dataset was large.
(C) That’s a test split.

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**49.**
**Q:** Can I export this model to ONNX format?
(A) Yes, with a simple conversion command.
(B) No, it’s about audio.
(C) I didn’t clean the data.

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**50.**
**Q:** Are the results included in the report?
(A) Yes, on the final page.
(B) We used a batch size of 128.
(C) It’s for classification.

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**51.**
**Q:** Did they pretrain the encoder?
(A) Yes, using a large corpus.
(B) It’s under the monitor.
(C) She ran the evaluation.

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**52.**
**Q:** Where should I submit the final notebook?
(A) Please upload it to the shared drive.
(B) I annotated the labels.
(C) No, the training is done.

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**53.**
**Q:** Is the feature importance chart ready?
(A) Yes, I generated it using SHAP.
(B) It’s a sequential dataset.
(C) The loss decreased.

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**54.**
**Q:** Why is the inference time so high?
(A) Probably due to the model size.
(B) It predicts sentiment.
(C) No, I didn’t save it.

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**55.**
**Q:** Has the script been deployed to production?
(A) No, we’re still testing.
(B) It’s a regression problem.
(C) I added some images.

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**56.**
**Q:** What does this accuracy score refer to?
(A) It’s from the test set performance.
(B) It uses a sigmoid layer.
(C) We added more filters.

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**57.**
**Q:** Are you using stratified sampling?
(A) Yes, to maintain class balance.
(B) No, the model didn’t train.
(C) It’s an audio file.

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**58.**
**Q:** Who is responsible for final evaluation?
(A) That would be Keisuke.
(B) I tokenized the input.
(C) Yes, the weights are frozen.

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**59.**
**Q:** Do we need to retrain the model again?
(A) Yes, after updating the dataset.
(B) It’s a low-resolution image.
(C) No, the shape is fine.

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**60.**
**Q:** What is causing the accuracy drop?
(A) Possibly noisy labels in the training data.
(B) It’s on the third row.
(C) We didn’t import it.

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## ✅ 正答一覧（No.41〜60）

41. (A)
42. (A)
43. (A)
44. (A)
45. (A)
46. (A)
47. (A)
48. (A)
49. (A)
50. (A)
51. (A)
52. (A)
53. (A)
54. (A)
55. (A)
56. (A)
57. (A)
58. (A)
59. (A)
60. (A)

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