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CompTIA DY0-001 Exam Syllabus Topics:
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CompTIA DataX Certification Exam Sample Questions (Q65-Q70):
NEW QUESTION # 65
Which of the following methods should a data scientist use just before switching to a potential replacement model?
Answer: A
Explanation:
# A/B testing allows a controlled experiment comparing the performance of two models - the current (A) vs.
the candidate (B) - on live data. It's an industry best practice to validate real-world behavior before full replacement.
Why the other options are incorrect:
* B: Performance monitoring helps detect drift but doesn't directly compare models.
* C: CI/CD automates deployment but doesn't evaluate performance differences.
* D: Containerization packages the model but doesn't test it comparatively.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"A/B testing is a recommended approach to validate model performance before switching versions in production."
* ML System Operations Guide, Chapter 6:"Use A/B testing to ensure new models outperform baselines before full rollout."
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NEW QUESTION # 66
Which of the following is a classic example of a constrained optimization problem?
Answer: B
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 # 67
A data scientist is presenting the recommendations from a monthslong modeling and experiment process to the company's Chief Executive Officer. Which of the following is the best set of artifacts to include in the presentation?
Answer: A
Explanation:
# For executive-level presentations, the focus should be on strategic outcomes. Therefore, concise results, clear actionable recommendations, visual summaries (charts), and minimal justifications are best. Technical details such as p-values, code, or full methods are too granular.
Why the other options are incorrect:
* A: Too method-heavy for executive audiences.
* C: Includes code reviews - not suitable for a CEO.
* D: Overly technical for high-level stakeholders.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"Executive communication should focus on outcome-driven recommendations, high-level insights, and actionable visuals."
* Harvard Business Review - Communicating Data to Executives:"Avoid technical detail. Use visuals and clearly stated recommendations supported by business-focused justifications."
NEW QUESTION # 68
Which of the following describes the appropriate use case for PCA?
Answer: B
Explanation:
# Principal Component Analysis (PCA) is an unsupervised technique used to reduce the dimensionality of large datasets by transforming correlated features into a smaller set of uncorrelated components (principal components) while retaining the most variance.
Why the other options are incorrect:
* B: Classification is a predictive modeling task; PCA is not inherently predictive.
* C: Regression models numerical relationships; PCA does not predict outcomes.
* D: Recommendation systems use collaborative or content filtering, not PCA directly.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"PCA is primarily used for reducing the number of variables while preserving data structure and minimizing information loss."
* Pattern Recognition and Machine Learning, Chapter 12:"PCA identifies principal axes of variation and is widely used in preprocessing for dimensionality reduction."
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NEW QUESTION # 69
Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?
Answer: C
Explanation:
# Pooling layers are used in Convolutional Neural Networks (CNNs) to reduce the spatial dimensions (width and height) of the feature maps. This helps in downsampling, reducing computational complexity, and controlling overfitting by summarizing the features (e.g., max pooling or average pooling).
Why the other options are incorrect:
* B: Input layers receive raw data and do not perform downsampling.
* C: Output layers generate the final prediction.
* D: Hidden layers process data but do not specifically perform downsampling unless designed to do so (e.g., convolutional or pooling sublayers).
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.3:"Pooling layers are used to downsample feature maps and are critical in CNNs for reducing dimensions."
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NEW QUESTION # 70
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