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Latest DY0-001 Version - DY0-001 Real QuestionsTest your knowledge of the CompTIA DataX Certification Exam (DY0-001) exam dumps with PassReview CompTIA DataX Certification Exam (DY0-001) practice questions. The software is designed to help with CompTIA DataX Certification Exam (DY0-001) exam dumps preparation. CompTIA DY0-001 practice test software can be used on devices that range from mobile devices to desktop computers. CompTIA DataX Certification Exam Sample Questions (Q46-Q51):NEW QUESTION # 46
Which of the following is a key difference between KNN and k-means machine-learning techniques?
A. KNN operates exclusively on continuous data, while k-means can work with both continuous and categorical data.
B. KNN is used for classification, while k-means is used for clustering.
C. KNN is used for finding centroids, while k-means is used for finding nearest neighbors.
D. KNN performs better with longitudinal data sets, while k-means performs better with survey data sets.
Answer: B
Explanation:
# K-Nearest Neighbors (KNN) is a supervised machine learning algorithm used primarily for classification and regression. It labels a new instance by majority vote (or averaging, in regression) of its k-nearest labeled neighbors.
# k-Means is an unsupervised learning algorithm used for clustering. It partitions unlabeled data into k groups based on feature similarity, using centroids.
Thus, the key difference is in their purpose:
* KNN # Classification (Supervised)
* K-Means # Clustering (Unsupervised)
Why the other options are incorrect:
* A: Both can technically operate on continuous or categorical data (with preprocessing).
* B: This is not a meaningful or standardized distinction.
* C: This reverses the actual roles. k-means finds centroids; KNN finds nearest neighbors.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.1 (Classification vs. Clustering):"KNN is a supervised learning algorithm for classification tasks. K-means is an unsupervised clustering technique that groups data by proximity to centroids."
* Data Science Handbook, Chapter 5:"One key distinction: KNN uses labeled data to classify or regress; k-means uses unlabeled data to identify groupings."
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NEW QUESTION # 47
A data analyst is analyzing data and would like to build conceptual associations. Which of the following is the best way to accomplish this task?
A. TF-IDF
B. n-grams
C. NER
D. POS
Answer: B
Explanation:
# n-grams (bigrams, trigrams, etc.) are sequences of N words used to analyze co-occurrences and build conceptual or contextual associations between terms in natural language processing (NLP). This helps in understanding the semantic structure of language and is ideal for finding relationships between words.
Why the other options are incorrect:
* B: NER (Named Entity Recognition) identifies entities like names or dates; it doesn't focus on conceptual associations.
* C: TF-IDF scores term importance relative to documents, not associations.
* D: POS (Part of Speech) tagging identifies word roles (noun, verb, etc.), not direct associations.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 6.3:"n-gram analysis is useful for discovering common patterns and associations in unstructured text data."
* Natural Language Processing with Python (NLTK Book), Chapter 3:"N-grams help capture collocations and associations between words that often co-occur, essential for understanding context."
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NEW QUESTION # 48
Which of the following distribution methods or models can most effectively represent the actual arrival times of a bus that runs on an hourly schedule?
A. Binomial
B. Poisson
C. Normal
D. Exponential
Answer: C
Explanation:
# A Normal distribution is appropriate for modeling variables that cluster around a central mean and have natural variability - such as bus arrival times around a scheduled time. Even though the bus is scheduled hourly, real-world factors (traffic, weather, etc.) will cause actual arrival times to vary normally around the scheduled mean.
Why the other options are incorrect:
* A: Binomial is for discrete yes/no trials, not continuous time modeling.
* B: Exponential models time between events, typically memoryless - not suitable for arrival distributions with a known mean and variance.
* D: Poisson models event counts per time interval, not the timing of continuous events like arrival times.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 1.3:"Normal distributions are appropriate for modeling real-world continuous variables that fluctuate around a central tendency, such as scheduled processes."
* Statistics for Data Science, Chapter 4 - Distributions:"Arrival times of periodic services often approximate a normal distribution when influenced by continuous variation."
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NEW QUESTION # 49
A computer vision model is trained to identify cats on a training set that is composed of both cat and dog images. The model predicts a picture of a cat is a dog. Which of the following describes this error?
A. Type II error
B. Error due to reality
C. False positive error
D. Sampling error
Answer: A
Explanation:
# A Type II error occurs when the model fails to identify a positive instance - in this case, a cat. That is, it incorrectly classifies a cat (positive class) as a dog (negative class). This is also referred to as a false negative.
Why the other options are incorrect:
* A: "Error due to reality" is not a recognized statistical concept.
* B: A false positive would mean misclassifying a dog as a cat (opposite error).
* C: Sampling error refers to discrepancies between the sample and population, not a misclassification.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 1.5:"Type II errors occur when a model incorrectly identifies a true positive as a negative - also known as a false negative."
* Pattern Recognition and Machine Learning, Chapter 9:"In binary classification, a Type II error means failing to detect a positive class instance, leading to a false negative result."
NEW QUESTION # 50
A data scientist built several models that perform about the same but vary in the number of features. Which of the following models should the data scientist recommend for production according to Occam's razor?
A. The model with the fewest features and highest performance
B. The model with the fewest features and the lowest performance
C. The model with the most features and the highest performance
D. The model with the most features and the lowest performance
Answer: A
Explanation:
# Occam's razor is a principle that suggests selecting the simplest solution that sufficiently explains the data.
In data science, this translates to favoring simpler models (fewer features) when performance is similar.
Therefore, the model with the fewest features and the highest performance is preferred - balancing simplicity and effectiveness.
Why the other options are incorrect:
* B: Poor performance undermines utility.
* C & D: More features add complexity and risk overfitting, making them less desirable when simpler models suffice.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 3.2:"Simplicity in models improves interpretability and robustness. When models perform similarly, the simpler model should be preferred."
* Data Science Principles, Chapter 5:"Occam's razor encourages the use of fewer features to minimize complexity while preserving accuracy."
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NEW QUESTION # 51
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