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CompTIA DataAI Certification Exam Valid Test Topics & DY0-001 Free Download Demo & CompTIA DataAI Certification Exam Practice Test Training
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CompTIA DY0-001 Exam Syllabus Topics:
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CompTIA DataAI Certification Exam Sample Questions (Q43-Q48):
NEW QUESTION # 43
A data scientist would like to model a complex phenomenon using a large data set composed of categorical, discrete, and continuous variables. After completing exploratory data analysis, the data scientist is reasonably certain that no linear relationship exists between the predictors and the target. Although the phenomenon is complex, the data scientist still wants to maintain the highest possible degree of interpretability in the final model. Which of the following algorithms best meets this objective?
- A. Artificial neural network
- B. Decision tree
- C. Random forest
- D. Multiple linear regression
Answer: B
Explanation:
Decision trees capture complex, nonlinear relationships with a transparent, rule-based structure. They remain highly interpretable (each split can be visualized and explained) unlike ensembles (random forests) or neural networks, and they don't rely on linear assumptions.
NEW QUESTION # 44
Which of the following image data augmentation techniques allows a data scientist to increase the size of a data set?
- A. Scaling
- B. Masking
- C. Clipping
- D. Cropping
Answer: D
Explanation:
By taking multiple crops from each original image (e.g., random or sliding-window crops), you generate distinct new training examples, directly increasing the dataset size.
NEW QUESTION # 45
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?
- A. k-nearest neighbors
- B. String indexing
- C. Word cloud
- D. Edit distance
Answer: D
Explanation:
Edit distance quantifies how many single-character insertions, deletions, or substitutions are needed to transform one string into another, making it a direct measure of their similarity.
NEW QUESTION # 46
A data scientist has built an image recognition model that distinguishes cars from trucks. The data scientist now wants to measure the rate at which the model correctly identifies a car as a car versus when it misidentifies a truck as a car. Which of the following would best convey this information?
- A. Correlation plot
- B. Confusion matrix
- C. AUC/ROC curve
- D. Box plot
Answer: B
Explanation:
A confusion matrix directly shows true positives (cars correctly identified) and false positives (trucks misidentified as cars), giving you exactly the rates you're interested in.
NEW QUESTION # 47
Which of the following layer sets includes the minimum three layers required to constitute an artificial neural network?
- A. An input layer, a dropout layer, and a hidden layer
- B. An input layer, a pooling layer, and an output layer
- C. An input layer, a convolutional layer, and a hidden layer
- D. An input layer, a hidden layer, and an output layer
Answer: D
Explanation:
By definition, an artificial neural network requires at least these three fundamental layers: the input layer to receive data, one or more hidden layers to perform transformations, and the output layer to produce predictions. Pooling, convolutional, and dropout layers are useful in specialized architectures (e.g., CNNs) but aren't part of the minimal ANN structure.
NEW QUESTION # 48
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