What to know
Identify the rule, formula, or decision criterion before reading the answer choices. CFA Level I distractors often use the right vocabulary with the wrong condition.
Quantitative Methods
Introduction to Big Data Techniques practice questions
Introduction to Big Data Techniques is part of CFA Level I Quantitative Methods. Quantitative Methods questions emphasize time value of money, probability, sampling, hypothesis testing, regression, and return statistics. Use this page to review the controlling ideas, then work through 11 questions with answer explanations and common traps.
Review the worked explanations before moving into adaptive practice. The app version can mix this topic with due reviews and weak related concepts.
Practice this topicHow to practice
Work each item under time pressure, then compare your reasoning with the step-by-step explanation and key takeaway.
Review signal
Missed questions should become scheduled reviews when the error comes from a concept gap, formula setup, or answer-choice trap.
Quantitative Methods
Introduction to Big Data Techniques
A machine learning model performs extremely well on historical training data but poorly on new out-of-sample data. The concern is most accurately described as:
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Introduction to Big Data Techniques
A model uses satellite images of parking lots to forecast retail sales. The vendor later reveals that images are missing on cloudy days, which occur more often in some regions. The primary concern is:
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Introduction to Big Data Techniques
Text from earnings call transcripts is best described as:
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Introduction to Big Data Techniques
A model trained on labeled bond default observations is most likely an example of:
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Introduction to Big Data Techniques
Overfitting is best described as a model that:
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Introduction to Big Data Techniques
A data scientist uses one dataset to estimate model parameters, a second to tune model complexity, and a third for final out-of-sample performance assessment. The second dataset is best described as the:
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Introduction to Big Data Techniques
The relationship among artificial intelligence, machine learning, and Big Data is most accurately described as:
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Introduction to Big Data Techniques
Before using alternative data in an investment model, the most appropriate first step is usually to:
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Introduction to Big Data Techniques
A portfolio model performs extremely well on historical backtests after hundreds of variables were tried, but it performs poorly in live trading. The most likely explanation is:
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Introduction to Big Data Techniques
A natural language processing model scores sentiment in central bank speeches and uses the score as an input to bond allocation. This application is best described as using:
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Introduction to Big Data Techniques
A machine learning credit model is trained on historical lending decisions that reflected biased approval practices. The most relevant risk is:
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