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Professional-Machine-Learning-Engineer – Professional Machine Learning Engineer

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Showing 1–3 of 7 questions

Question 1

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?

Choose 2 answers

Select all that apply, then click Submit answer.

  • ○

    Decrease the number of parallel trials
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    Decrease the range of floating-point values
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    Set the early stopping parameter to TRUE
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    Change the search algorithm from Bayesian search to random search.
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    Decrease the maximum number of trials during subsequent training phases.
Question 2

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

Select all that apply, then click Submit answer.

  • ○

    Use the interleave option for reading data
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    Reduce the value of the repeat parameter
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    Increase the buffer size for the shuffle option.
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    Set the prefetch option equal to the training batch size
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    Decrease the batch size argument in your transformation
Question 3

You are going to train a DNN regression model with Keras APIs using this code:

C:\Users\wk\Desktop\mudassar\Untitled.png

How many trainable weights does your model have? (The arithmetic below is correct.)

Select an option, then click Submit answer.

  • ○

    501*256+257*128+2 = 161154
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    500*256+256*128+128*2 = 161024
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    501*256+257*128+128*2=161408
  • ○

    500*256*0 25+256*128*0 25+128*2 = 40448