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Exam Professional Machine Learning Engineer topic 1 question 133 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 133
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
• For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
• For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.

You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

  • A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
  • B. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
  • C. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
  • D. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by mil_spyro at Dec. 13, 2022, 6:18 p.m.

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guilhermebutzke
Highly Voted 1 year, 4 months ago
Selected Answer: B
B: Here's why: **Embedding Dimension:** UNIT_LINEAR_SCALE is appropriate for integer hyperparameters with a continuous range like the embedding dimension. It linearly scales the search space from minValue to maxValue. **Learning Rate:** UNIT_LOG_SCALE is generally recommended for hyperparameters with values spanning multiple orders of magnitude like the learning rate (10e-05 - 10e-02). This ensures equal sampling probability across different log-scaled ranges. **Parallel Trials:** as the documentation specifies, parallelization speeds up. However, this speedup comes at the cost of potentially sacrificing the quality of the results. Since training time is not a factor in this case, the benefit of speeding things up with many parallel trials is less valuable. https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning#parallel-trials
upvoted 15 times
LFavero
1 year, 3 months ago
this is the perfect answer and explanation.
upvoted 2 times
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YangG
Highly Voted 2 years, 6 months ago
Selected Answer: B
Vote B
upvoted 11 times
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NamitSehgal
Most Recent 3 months, 3 weeks ago
Selected Answer: A
maxParallelTrials: Small number of parallel trials: A small number of trials would limit the exploration of the hyperparameter space and might prevent you from finding the best possible model. Since training time is not a concern for you, and you want to maximize model accuracy, using a large number of parallel trials is beneficial.
upvoted 1 times
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AB_C
6 months, 3 weeks ago
Selected Answer: A
as training time is not an issue hence the answer should be A
upvoted 3 times
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pinimichele01
1 year, 2 months ago
Training time is not a concern -> B (the benefit of speeding things up with many parallel trials is less valuable)
upvoted 1 times
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gscharly
1 year, 2 months ago
Selected Answer: B
Vote B
upvoted 1 times
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Mickey321
1 year, 7 months ago
Selected Answer: A
because training time is not a concern and you want to maximize accuracy, using a large number of maxParallelTrials (option A) allows thoroughly searching the hyperparameter space.
upvoted 3 times
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Voyager2
2 years ago
Selected Answer: B
B. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials. https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning First we should choos an option with small trials: "Before starting a job with a large number of trials, you may want to start with a small number of trials to gauge the effect your chosen hyperparameters have on your model's accuracy." Now, the embeddings should be linear https://cloud.google.com/blog/products/gcp/hyperparameter-tuning-on-google-cloud-platform-is-now-faster-and-smarter
upvoted 1 times
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M25
2 years, 1 month ago
Selected Answer: B
Went with B
upvoted 1 times
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JamesDoe
2 years, 2 months ago
Selected Answer: B
https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning#parallel-trials "Running parallel trials has the benefit of reducing the time the training job takes (real time—the total processing time required is not typically changed). However, running in parallel can reduce the effectiveness of the tuning job overall." Since opt. for accuracy and ignore training time, use above. Linear for learning rate doesn't really make sense, think that one is obvious imo.
upvoted 2 times
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TNT87
2 years, 3 months ago
Selected Answer: B
Answer is B , even my explanation is on B not C Option B is the best choice: Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials. The reason for this choice is as follows: For the embedding dimension, it is better to use a logarithmic scale because the effect of increasing the dimensionality is likely to diminish as the dimension grows larger. Therefore, the logarithmic scale will allow the tuning algorithm to explore a wider range of values with less bias towards higher values
upvoted 1 times
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TNT87
2 years, 3 months ago
Selected Answer: C
Option C is the best choice: Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials. The reason for this choice is as follows: For the embedding dimension, it is better to use a logarithmic scale because the effect of increasing the dimensionality is likely to diminish as the dimension grows larger. Therefore, the logarithmic scale will allow the tuning algorithm to explore a wider range of values with less bias towards higher values
upvoted 1 times
TNT87
2 years, 3 months ago
Meant to choose B ahhhh
upvoted 1 times
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John_Pongthorn
2 years, 4 months ago
Selected Answer: B
Learning Rage is subtle and take time so, it use Log Scale
upvoted 2 times
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ares81
2 years, 5 months ago
Selected Answer: B
It's B!
upvoted 1 times
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hiromi
2 years, 5 months ago
Selected Answer: A
A - https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#HyperparameterSpec - https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/StudySpec
upvoted 1 times
hiromi
2 years, 5 months ago
Sorry, B is the answer
upvoted 1 times
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mil_spyro
2 years, 6 months ago
Selected Answer: D
Vote D, this can help the tuning algorithm explore a wider range of values for the learning rate, while also focusing on a smaller range of values for the embedding dimension.
upvoted 2 times
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C (25%)
B (20%)
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