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A movie production company would like to find the actors appearing in its top movies using data from the tables below. The resulting data must show all movies in Table 1, enriched with actors listed in Table 2.
Which of the following query operations achieves the desired data set?
Answer : D
A LEFT JOIN returns every row from Table 1 (all top movies) and brings in matching actors from Table 2 where the Movie = Acted_In, leaving NULLs for movies without listed actors.
A company created a very popular collectible card set. Collectors attempt to collect the entire set, but the availability of each card varies, with because some cards have higher production volumes than others. The set contains a total of 12 cards. The attributes of the cards are below:
A data scientist is provided a historical record of cards purchased, which was acquired by a local collectors' association. The data scientist needs to design an initial model iteration to predict whether or not the animal on the card lives in the sea or on land given the provided attributes. Which of the following is the best way to accomplish this task?
Answer : D
You have categorical inputs (wrapper color, shape, animal) and a binary target (sea vs. land). A decision tree natively handles categorical features and yields clear, rule-based splits that predict habitat, making it the most appropriate choice.
A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the dat
a. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
Answer : B
Which of the following is best solved with graph theory?
Answer : B
The traveling-salesman problem is a prototypical graph theory challenge, finding the shortest tour through a graph's nodes, whereas the other tasks rely on different domains (OCR on image processing, fraud detection often on statistical/anomaly methods, bandit problems on sequential decision theory).
A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?
Answer : A
Generating embeddings transforms each sentence into a dense numerical vector in a semantic space, where conceptually similar sentences lie close together, enabling straightforward similarity calculations (e.g., cosine similarity) to group or identify related sentences.
