Answer-first summary for fast verification
Answer: Modify the 'scale-tier' parameter.
The correct answer is 'B'. By modifying the 'scale-tier' parameter to a higher tier, you can allocate more powerful and numerous machines to the training job. This change will reduce the training time significantly without impacting the accuracy of your model. Options 'A', 'C', and 'D' involve tuning hyperparameters that directly affect the model's performance and thus are not suitable for minimizing training time without potentially compromising accuracy.
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You are training an LSTM-based model on Google AI Platform to create text summaries. Below is the job submission script you are using: gcloud ai-platform jobs submit training TRAINER_PACKAGE_PATH --module-name JOB_DIR --region $REGION --scale-tier basic -- --epochs 20 --batch_size=32 --learning_rate=0.001. Your goal is to minimize the training time without significantly compromising the accuracy of the model. Considering the current settings, what should you do to achieve this balance?
A
Modify the 'epochs' parameter.
B
Modify the 'scale-tier' parameter.
C
Modify the 'batch size' parameter.
D
Modify the 'learning rate' parameter.
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