CompTIA DY0-001 Valid Braindumps Book & Latest Study DY0-001 Questions
CompTIA DY0-001 Valid Braindumps Book & Latest Study DY0-001 Questions
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CompTIA DataX Certification Exam Sample Questions (Q86-Q91):
NEW QUESTION # 86
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?
- A. Extrapolation
- B. One-hot encoding
- C. Embeddings
- D. Sampling
Answer: C
Explanation:
# Embeddings (e.g., word2vec, sentence transformers) are vector representations of text that capture semantic similarity. They allow comparison of conceptual meaning between sentences in a high-dimensional space, which is essential for tasks like semantic similarity or clustering.
Why the other options are incorrect:
* B: Extrapolation predicts values beyond a dataset's range - not relevant here.
* C: Sampling reduces data volume but doesn't aid in similarity analysis.
* D: One-hot encoding captures presence of words but lacks semantic understanding.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Embeddings transform text into numeric vectors, enabling similarity computation and semantic analysis."
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NEW QUESTION # 87
A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)
- A. One-hot encoding
- B. Linearization
- C. Label encoding
- D. Normalization
- E. Pivoting
- F. Scaling
Answer: A,C
NEW QUESTION # 88
Under perfect conditions, E. coli bacteria would cover the entire earth in a matter of days. Which of the following types of models is the best for explaining this type of growth?
- A. Logarithmic
- B. Linear
- C. Exponential
- D. Polynomial
Answer: C
Explanation:
# Bacterial growth under ideal conditions follows exponential behavior: the population doubles at regular intervals. This results in a rapid increase that aligns with the formula: N(t) = N#e