- A
Enable early stopping using a Bayesian optimization algorithm.
Why wrong: Early stopping prunes unpromising trials, saving time, but does not recover from transient errors.
- B
Set the maxFailedTrials parameter to a high value (e.g., 10).
This allows the tuning job to tolerate transient failures and continue searching without aborting, improving completion rate and model quality.
- C
Use larger machine types for each trial.
Why wrong: Larger machines may reduce failures but increase cost per trial, potentially increasing overall cost.
- D
Increase the number of parallel trials.
Why wrong: More parallel trials may speed up search but does not reduce cost or handle failure recovery.
Question 715 of 1,000
Reduce Costs and Improve Model Quality by Configuring maxFailedTrials
This PDE practice question tests your understanding of ensuring solution quality. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data science team uses AI Platform Training with hyperparameter tuning. They observe that some trials fail due to transient errors. To improve solution quality and reduce costs, what should they do?
Quick Answer
The answer is to set the maxFailedTrials parameter to a high value, such as 10. This configuration directly addresses AI Platform hyperparameter tuning transient errors by allowing the job to tolerate a specified number of trial failures without aborting the entire tuning run, thereby improving model quality through more completed trials while reducing costs by avoiding the need to re-run expensive, failed attempts. On the Google Professional Data Engineer exam, this concept tests your understanding of how to balance resource efficiency with robustness in distributed training jobs; a common trap is confusing this with early stopping (which prunes unpromising trials, not failed ones) or increasing parallelism (which still pays for failures). Remember the mnemonic: "Failures are fine, just set a high maxFailedTrials line."
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the maxFailedTrials parameter to a high value (e.g., 10).
Option B is correct because setting maxFailedTrials to a high value (e.g., 10) allows the hyperparameter tuning job to continue even when some trials fail due to transient errors. This improves solution quality by ensuring that enough successful trials complete to explore the search space, and it reduces costs by avoiding premature job termination that would waste resources on restarts.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable early stopping using a Bayesian optimization algorithm.
Why it's wrong here
Early stopping prunes unpromising trials, saving time, but does not recover from transient errors.
- ✓
Set the maxFailedTrials parameter to a high value (e.g., 10).
Why this is correct
This allows the tuning job to tolerate transient failures and continue searching without aborting, improving completion rate and model quality.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use larger machine types for each trial.
Why it's wrong here
Larger machines may reduce failures but increase cost per trial, potentially increasing overall cost.
- ✗
Increase the number of parallel trials.
Why it's wrong here
More parallel trials may speed up search but does not reduce cost or handle failure recovery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse early stopping (Option A) with handling transient errors, but early stopping is for performance-based pruning, not for fault tolerance. The trap is confusing optimization strategies with error-handling parameters in AI Platform Training.
Detailed technical explanation
How to think about this question
In AI Platform Training, the maxFailedTrials parameter specifies the maximum number of trials that can fail before the entire hyperparameter tuning job is stopped. Transient errors (e.g., temporary network issues or resource contention) are common in distributed training; setting this value appropriately (e.g., 10) ensures the job tolerates a reasonable number of failures without aborting. Under the hood, the service tracks trial statuses and only stops the job when the count of failed trials exceeds maxFailedTrials, allowing the Bayesian optimization algorithm to continue learning from successful trials.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this PDE question test?
Ensuring solution quality — This question tests Ensuring solution quality — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the maxFailedTrials parameter to a high value (e.g., 10). — Option B is correct because setting maxFailedTrials to a high value (e.g., 10) allows the hyperparameter tuning job to continue even when some trials fail due to transient errors. This improves solution quality by ensuring that enough successful trials complete to explore the search space, and it reduces costs by avoiding premature job termination that would waste resources on restarts.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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