ML Problem Framing Quiz - Competitive Exams GCP_ML_Engineer
Free practice quiz: ML Problem Framing (Competitive Exams GCP_ML_Engineer). 50 multiple-choice questions with answers and explanations. Play online, no sign-up needed.
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When framing a machine learning problem, what is the primary purpose of defining a clear objective?
- To determine the cloud provider
- To guide selection of data, features, and evaluation metrics
- To choose the programming language
- To ensure the problem is solvable with ML
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Answer: B. To guide selection of data, features, and evaluation metrics
What does the term 'label' refer to in supervised learning?
- The input features
- The model parameters
- The output or target variable
- The evaluation metric
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Answer: C. The output or target variable
Which of the following is an example of a semi-supervised learning problem?
- Using only unlabeled data
- Using a small labeled dataset and a large unlabeled dataset
- Using reinforcement learning with rewards
- Using only labeled data
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Answer: B. Using a small labeled dataset and a large unlabeled dataset
What term describes the situation where the offline metric improves but the business KPI does not, because the offline metric is insufficiently tied to the actual goal?
- Data drift
- Metric misalignment
- Concept drift
- Overfitting
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Answer: B. Metric misalignment
When the labels for a task are themselves noisy or generated by heuristics, how should the problem framing account for this?
- Treat the label noise as a modeling challenge and consider probabilistic labels or noise-robust loss functions
- Switch to an unsupervised approach regardless of the task
- Increase model complexity until the noise fits
- Ignore label quality since models are robust
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Answer: A. Treat the label noise as a modeling challenge and consider probabilistic labels or noise-robust loss functions