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CompTIA CompTIA DataX Certification Exam - DY0-001 的訓練題庫很全面，包含全真的訓練題，和 CompTIA CompTIA DataX Certification Exam - DY0-001 真實考試相關的考試練習題和答案。而售後服務不僅能提供最新的 CompTIA CompTIA DataX Certification Exam - DY0-001 練習題和答案以及動態消息，還不斷的更新 CompTIA DataX Certification Exam - DY0-001 題庫資料的題目和答案，方便客戶對考試做好充分的準備。
購買後，立即下載 DY0-001 試題 (CompTIA DataX Certification Exam): 成功付款後, 我們的體統將自動通過電子郵箱將你已購買的產品發送到你的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查你的垃圾郵件。）
CompTIA DY0-001 考試大綱：
| 主題 | 簡介 |
|---|
| 主題 1 | - Machine Learning: This section of the exam measures skills of a Machine Learning Engineer and covers foundational ML concepts such as overfitting, feature selection, and ensemble models. It includes supervised learning algorithms, tree-based methods, and regression techniques. The domain introduces deep learning frameworks and architectures like CNNs, RNNs, and transformers, along with optimization methods. It also addresses unsupervised learning, dimensionality reduction, and clustering models, helping candidates understand the wide range of ML applications and techniques used in modern analytics.
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| 主題 2 | - Specialized Applications of Data Science: This section of the exam measures skills of a Senior Data Analyst and introduces advanced topics like constrained optimization, reinforcement learning, and edge computing. It covers natural language processing fundamentals such as text tokenization, embeddings, sentiment analysis, and LLMs. Candidates also explore computer vision tasks like object detection and segmentation, and are assessed on their understanding of graph theory, anomaly detection, heuristics, and multimodal machine learning, showing how data science extends across multiple domains and applications.
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| 主題 3 | - Operations and Processes: This section of the exam measures skills of an AI
- ML Operations Specialist and evaluates understanding of data ingestion methods, pipeline orchestration, data cleaning, and version control in the data science workflow. Candidates are expected to understand infrastructure needs for various data types and formats, manage clean code practices, and follow documentation standards. The section also explores DevOps and MLOps concepts, including continuous deployment, model performance monitoring, and deployment across environments like cloud, containers, and edge systems.
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| 主題 4 | - Modeling, Analysis, and Outcomes: This section of the exam measures skills of a Data Science Consultant and focuses on exploratory data analysis, feature identification, and visualization techniques to interpret object behavior and relationships. It explores data quality issues, data enrichment practices like feature engineering and transformation, and model design processes including iterations and performance assessments. Candidates are also evaluated on their ability to justify model selections through experiment outcomes and communicate insights effectively to diverse business audiences using appropriate visualization tools.
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| 主題 5 | - Mathematics and Statistics: This section of the exam measures skills of a Data Scientist and covers the application of various statistical techniques used in data science, such as hypothesis testing, regression metrics, and probability functions. It also evaluates understanding of statistical distributions, types of data missingness, and probability models. Candidates are expected to understand essential linear algebra and calculus concepts relevant to data manipulation and analysis, as well as compare time-based models like ARIMA and longitudinal studies used for forecasting and causal inference.
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參考：https://www.comptia.org/certifications/datax
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