Practice Test GeeksData Science Practice Test
← All Data Science Flashcard Decks

Data Science-DATA Science 1 Flashcards

6 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 6 Data Science-DATA Science 1 flashcards as text

  1. Which measure of central tendency is most resistant to the influence of outliers?

    Answer: Median

    The median is the middle value of a sorted dataset and is unaffected by extreme values, making it the most robust measure of central tendency when outliers are present.

  2. What is the primary purpose of cross-validation in machine learning?

    Answer: To assess how well a model generalizes to unseen data

    Cross-validation partitions data into multiple folds and repeatedly trains/tests the model, giving a reliable estimate of how the model will perform on new, unseen data.

  3. In a neural network, what is the role of the activation function?

    Answer: To introduce non-linearity into the model

    Activation functions like ReLU or sigmoid introduce non-linearity, allowing neural networks to learn complex, non-linear relationships that a purely linear model could not capture.

  4. Which SQL clause is used to filter rows after a GROUP BY aggregation has been applied?

    Answer: HAVING

    HAVING filters groups produced by GROUP BY, whereas WHERE filters individual rows before aggregation occurs. HAVING is the correct clause for conditions on aggregated values.

  5. What does a confusion matrix's 'precision' metric measure?

    Answer: The proportion of predicted positives that are truly positive

    Precision = True Positives / (True Positives + False Positives). It measures how many of the model's positive predictions were actually correct, reflecting the cost of false alarms.

  6. Which data transformation technique scales features so they have a mean of 0 and a standard deviation of 1?

    Answer: Standardization (Z-score scaling)

    Standardization subtracts the mean and divides by the standard deviation, producing a distribution centered at 0 with unit variance. This differs from Min-Max normalization, which scales values to a fixed range like [0, 1].