CompTIA DY0-001 Boot Camp & Frenquent DY0-001 Update
CompTIA DY0-001 Boot Camp & Frenquent DY0-001 Update
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CompTIA DataX Certification Exam Sample Questions (Q27-Q32):
NEW QUESTION # 27
Which of the following is a classic example of a constrained optimization problem?
- A. Calculating local maximum
- B. The cold start problem
- C. Calculating gradient descent
- D. The traveling salesman
Answer: D
Explanation:
# The Traveling Salesman Problem (TSP) is a classic example of a constrained optimization problem. The goal is to find the shortest possible route that visits a set of locations once and returns to the origin point - under constraints such as distance, order, and time.
Why the other options are incorrect:
* A: The cold start problem is related to recommender systems, not optimization.
* C: Calculating a local maximum is part of optimization but not necessarily constrained.
* D: Gradient descent is an optimization method, but not itself a problem with constraints.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 3.4:"Constrained optimization involves solving problems under defined limitations - e.g., distance or time constraints in routing."
* Optimization Techniques in Data Science, Chapter 6:"TSP is a benchmark in combinatorial optimization, representing a multi-variable problem with strict constraints."
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NEW QUESTION # 28
Which of the following is a key difference between KNN and k-means machine-learning techniques?
- A. KNN performs better with longitudinal data sets, while k-means performs better with survey data sets.
- B. KNN is used for classification, while k-means is used for clustering.
- C. KNN operates exclusively on continuous data, while k-means can work with both continuous and categorical data.
- D. KNN is used for finding centroids, while k-means is used for finding nearest neighbors.
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 # 29
A data scientist wants to predict a person's travel destination. The options are:
* Branson, Missouri, United States
* Mount Kilimanjaro, Tanzania
* Disneyland Paris, Paris, France
* Sydney Opera House, Sydney, Australia
Which of the following models would best fit this use case?
- A. Principal component analysis
- B. Linear discriminant analysis
- C. k-means modeling
- D. Latent semantic analysis
Answer: B
Explanation:
# Linear Discriminant Analysis (LDA) is a supervised classification method used to predict a categorical target (such as travel destination) based on multiple input features. It models decision boundaries between classes - which is appropriate when predicting a fixed set of destinations.
Why the other options are incorrect:
* B: k-means is unsupervised and doesn't use labeled output like travel destination.
* C: Latent Semantic Analysis is used for extracting relationships from textual data - not categorical prediction.
* D: PCA reduces dimensionality but doesn't classify.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 4.1:"Linear Discriminant Analysis is used when the response variable is categorical and the objective is classification."
* Classification Techniques Guide, Chapter 7:"LDA excels in multi-class prediction when the input data is continuous and the output is a known category."
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NEW QUESTION # 30
Which of the following best describes the minimization of the residual term in a LASSO linear regression?
- A. e
- B. |e|
- C. e²
- D. 0
Answer: C
Explanation:
# LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.
Why the other options are incorrect:
* A: |e| is absolute error, not used in standard LASSO objective.
* B: e is the error term, but minimization applies to its squared version.
* C: Minimizing to exactly 0 is idealistic but not realistic.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"LASSO minimizes squared errors with an additional L1 regularization term."
* Elements of Statistical Learning, Chapter 6:"LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint."
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NEW QUESTION # 31
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. Exponential
- B. Polynomial
- C. Linear
- D. Logarithmic
Answer: A
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