TOEIC Part 3



### ✅【Set 1】AIモデル導入の打ち合わせ

**Conversation:**
**Man:** Did you check the results from the new recommendation engine?
**Woman:** Yes, the click-through rate improved by almost 10%.
**Man:** That’s great. Are we going to deploy it across all platforms?
**Woman:** Not yet. We’ll start with the mobile app first and monitor performance there.

**Questions:**
**Q1.** What are the speakers mainly discussing?
(A) A data labeling strategy
(B) A model evaluation metric
(C) A system rollout plan

**Q2.** What does the woman say about the new model?
(A) It improved engagement.
(B) It failed to load on mobile.
(C) It was trained with more data.

**Q3.** What will the company do next?
(A) Launch the model on all devices
(B) Monitor performance on mobile
(C) Redesign the entire system

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### ✅【Set 2】トレーニングログの確認

**Conversation:**
**Woman:** The training logs from last night show a sudden drop in accuracy.
**Man:** Did you change any hyperparameters?
**Woman:** No, but the data pipeline had missing values.
**Man:** That could be it. We should fix the pipeline and rerun the training.

**Questions:**
**Q4.** What problem are the speakers discussing?
(A) Incomplete data
(B) Software version mismatch
(C) Poor user feedback

**Q5.** What does the woman say about the hyperparameters?
(A) She reset them.
(B) She didn’t change them.
(C) She reduced the batch size.

**Q6.** What will they most likely do next?
(A) Change the model type
(B) Fix the data pipeline
(C) Increase GPU usage

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### ✅【Set 3】AIカンファレンスの発表者

**Conversation:**
**Man:** Do you know who’s presenting on explainable AI at the conference?
**Woman:** I heard it’s Professor Saito from Kyoto University.
**Man:** Oh, he’s great. His last paper on SHAP values was impressive.
**Woman:** Yes, and his slides are always so clear.

**Questions:**
**Q7.** What event are the speakers discussing?
(A) A product launch
(B) A research conference
(C) A hiring interview

**Q8.** What is Professor Saito known for?
(A) His work on data pipelines
(B) His visualization skills
(C) His efficiency in deployment

**Q9.** How does the woman describe his presentations?
(A) Detailed but confusing
(B) Visually appealing
(C) Clear and effective

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### ✅【Set 4】プレゼン準備

**Conversation:**
**Woman:** Are your slides ready for the client meeting?
**Man:** Almost. I’m still updating the evaluation metrics section.
**Woman:** Be sure to include the confusion matrix—it really helped last time.
**Man:** Good point. I’ll add that before sending the final version.

**Questions:**
**Q10.** What are the speakers preparing for?
(A) A software update
(B) A client presentation
(C) A job interview

**Q11.** What is the man currently working on?
(A) Model hyperparameters
(B) A confusion matrix
(C) Evaluation metrics

**Q12.** What does the woman suggest?
(A) Including a specific chart
(B) Reducing training time
(C) Rewriting the script

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### ✅【Set 5】GPU予約のトラブル

**Conversation:**
**Man:** I tried to run my training script, but the GPU cluster was already full.
**Woman:** I think Yuta reserved several nodes for his large-scale experiment.
**Man:** That explains it. I’ll wait until he’s done.
**Woman:** Or you can use the smaller cluster—it’s usually available in the evening.

**Questions:**
**Q13.** What problem does the man mention?
(A) His training failed due to memory error
(B) The GPU cluster was fully occupied
(C) He lost access to the server

**Q14.** What does the woman say about Yuta?
(A) He optimized the cluster settings
(B) He ran a data-cleaning job
(C) He reserved multiple GPU nodes

**Q15.** What does the woman suggest?
(A) Waiting until the next morning
(B) Using a different cluster later
(C) Asking Yuta to cancel his job

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## ✅ 正答一覧（Answer Key）

| No. | Answer |
| --- | ------ |
| 1   | C      |
| 2   | A      |
| 3   | B      |
| 4   | A      |
| 5   | B      |
| 6   | B      |
| 7   | B      |
| 8   | B      |
| 9   | C      |
| 10  | B      |
| 11  | C      |
| 12  | A      |
| 13  | B      |
| 14  | C      |
| 15  | B      |

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### ✅【Set 1】新しい分類モデルの結果

**Conversation:**
**Man:** Did you review the results from our new classification model?
**Woman:** I did. The precision improved, but recall dropped slightly.
**Man:** Should we adjust the threshold then?
**Woman:** Maybe. We can try tuning it and see how it affects F1 score.

**Questions:**
**Q1.** What are the speakers discussing?
(A) Dataset licensing issues
(B) Evaluation metrics of a model
(C) Hyperparameter tuning of a clustering algorithm

**Q2.** What concern does the woman raise?
(A) Poor model convergence
(B) Low recall score
(C) Training time is too long

**Q3.** What will they most likely do next?
(A) Collect more training data
(B) Retrain with a new optimizer
(C) Adjust the threshold

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### ✅【Set 2】チームミーティングの準備

**Conversation:**
**Woman:** Have you prepared the agenda for tomorrow’s AI team meeting?
**Man:** Not yet. I’m waiting for final accuracy numbers from the NLP team.
**Woman:** I see. We also need to confirm the speaker for the ethics session.
**Man:** Right, I’ll message Keiko to ask if she’s still available.

**Questions:**
**Q4.** What is the meeting mainly about?
(A) Scheduling software updates
(B) Reviewing machine learning results
(C) Planning a product launch

**Q5.** Why is the man delaying the agenda?
(A) He hasn’t booked the meeting room
(B) He’s waiting for evaluation results
(C) He lost the previous version

**Q6.** What will the man probably do next?
(A) Contact a colleague
(B) Cancel the session
(C) Update a dataset

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### ✅【Set 3】ハイパーパラメータ設定の確認

**Conversation:**
**Man:** The model’s validation loss is still fluctuating.
**Woman:** Did you reduce the learning rate like I suggested?
**Man:** I set it to 0.01, but maybe that’s still too high.
**Woman:** You could try a scheduler to adjust it dynamically.

**Questions:**
**Q7.** What problem are they trying to solve?
(A) Overfitting to training data
(B) Inconsistent validation loss
(C) Missing training labels

**Q8.** What has the man done so far?
(A) Used dropout
(B) Replaced the model
(C) Lowered the learning rate

**Q9.** What does the woman recommend?
(A) Using early stopping
(B) Implementing a learning rate scheduler
(C) Changing the optimizer

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### ✅【Set 4】転移学習の適用

**Conversation:**
**Woman:** Have you tried using a pretrained model this time?
**Man:** Yes, I used BERT for the classification task.
**Woman:** And how did it perform compared to training from scratch?
**Man:** Much better. It reached 92% accuracy in just five epochs.

**Questions:**
**Q10.** What technique is being discussed?
(A) Batch normalization
(B) Transfer learning
(C) Data augmentation

**Q11.** What model did the man use?
(A) GPT-2
(B) ResNet
(C) BERT

**Q12.** What does the man say about the result?
(A) It was unstable across runs
(B) It converged quickly
(C) It required longer training

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### ✅【Set 5】バグの原因と対応

**Conversation:**
**Man:** The script crashed during data preprocessing.
**Woman:** Did you check for null values? That’s usually the issue.
**Man:** Ah, no—I forgot to drop the missing rows.
**Woman:** That should fix it. Let me know if it runs smoothly now.

**Questions:**
**Q13.** What problem is the man facing?
(A) A corrupted dataset
(B) A bug in preprocessing
(C) An outdated script version

**Q14.** What does the woman suggest?
(A) Restarting the kernel
(B) Dropping null values
(C) Updating Python

**Q15.** What is the man likely to do next?
(A) Create a new script
(B) Change the model type
(C) Rerun the code after fixing the issue

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## ✅ 正答一覧（Answer Key）

| No. | Answer |
| --- | ------ |
| 1   | B      |
| 2   | B      |
| 3   | C      |
| 4   | B      |
| 5   | B      |
| 6   | A      |
| 7   | B      |
| 8   | C      |
| 9   | B      |
| 10  | B      |
| 11  | C      |
| 12  | B      |
| 13  | B      |
| 14  | B      |
| 15  | C      |

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