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NEW QUESTION 46
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

  • A. Modify the ‘learning rate’ parameter
  • B. Modify the ‘scale-tier’ parameter
  • C. Modify the ‘epochs’ parameter
  • D. Modify the batch size’ parameter

Answer: B

Explanation:
https://cloud.google.com/ai-platform/training/docs/machine-types#scale_tiers Google may optimize the configuration of the scale tiers for different jobs over time, based on customer feedback and the availability of cloud resources. Each scale tier is defined in terms of its suitability for certain types of jobs. Generally, the more advanced the tier, the more machines are allocated to the cluster, and the more powerful the specifications of each virtual machine. As you increase the complexity of the scale tier, the hourly cost of training jobs, measured in training units, also increases. See the pricing page to calculate the cost of your job.

 

NEW QUESTION 47
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

  • A. Reduce the value of the repeat parameter
  • B. Set the prefetch option equal to the training batch size
  • C. Use the interleave option for reading data
  • D. Increase the buffer size for the shuffle option.
  • E. Decrease the batch size argument in your transformation

Answer: B,C

 

NEW QUESTION 48
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

  • A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
  • B. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
  • C. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
  • D. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer: D

 

NEW QUESTION 49
……

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