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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Specialized Applications of Data Science | 13% | - Industry-specific analytics use cases - Natural Language Processing (NLP) - Time-series analysis - Computer Vision - Anomaly detection and signal processing |
| Topic 2: Operations and Processes | 22% | - Data pipeline design and maintenance - Data governance and quality management - Version control and reproducibility - Security and compliance in data operations - Model deployment and monitoring |
| Topic 3: Mathematics and Statistics | 17% | - Linear algebra fundamentals - Calculus and optimization concepts - Bayesian reasoning and modeling - Statistical inference and hypothesis testing - Probability theory and distributions |
| Topic 4: Machine Learning | 24% | - Hyperparameter tuning and optimization - Algorithm selection and implementation - Deep learning fundamentals - Supervised, unsupervised, and reinforcement learning - Ethics and bias in machine learning |
| Topic 5: Modeling, Analysis, and Outcomes | 24% | - Model selection and evaluation metrics - Feature engineering and selection - Data preparation and exploratory data analysis - Result interpretation and business communication - Predictive and prescriptive analytics |
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NEW QUESTION # 76
A data scientist is preparing to brief a non-technical audience that is focused on analysis and results. During the modeling process, the data scientist produced the following artifacts:
Which of the following artifacts should the data scientist include in the briefing? (Choose two.)
Answer: A,F
Explanation:
# Non-technical business stakeholders value outcome-oriented visuals (charts, dashboards) and the purpose
/justification for the modeling work. These artifacts directly communicate impact without overwhelming technical complexity.
Why the other options are incorrect:
* C & D: Too technical for a non-technical audience.
* E: Useful, but may be too detailed depending on the level of abstraction desired.
* F: Data dictionary is better suited for technical handoff - not executive review.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"Business-oriented presentations should emphasize clear visualizations, insights, and executive summaries of model goals."
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NEW QUESTION # 77
Which of the following is best solved with graph theory?
Answer: C
Explanation:
The traveling-salesman problem is a prototypical graph theory challenge, finding the shortest tour through a graph's nodes, whereas the other tasks rely on different domains (OCR on image processing, fraud detection often on statistical/anomaly methods, bandit problems on sequential decision theory).
NEW QUESTION # 78
A data scientist wants to digitize historical hard copies of documents. Which of the following is the best method for this task?
Answer: D
Explanation:
# Optical Character Recognition (OCR) is the process of converting scanned images or hard copy text into machine-encoded text. It is the standard technique for digitizing printed or handwritten content.
Why the other options are incorrect:
* A: Word2vec is for generating word embeddings from digital text.
* C: Latent Semantic Analysis analyzes semantic structure of existing digital documents.
* D: Semantic segmentation is used in image processing for pixel-wise classification - not text extraction.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 6.3:"OCR converts scanned physical documents into text files that can be searched, analyzed, or stored digitally."
* Practical NLP Applications, Chapter 2:"OCR is a prerequisite for turning printed or written material into structured data suitable for text analytics."
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NEW QUESTION # 79
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?
Answer: A
Explanation:
An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the time‐series history is available.
NEW QUESTION # 80
A data scientist trained a model for departments to share. The departments must access the model using HTTP requests. Which of the following approaches is appropriate?
Answer: B
Explanation:
Exposing the model behind an HTTP endpoint (for example, a REST API) allows other departments to send requests and receive predictions directly over HTTP. The other options don't inherently provide a request-response interface for sharing a model.
NEW QUESTION # 81
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