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CompTIA DY0-001 Prüfungsplan:
| Thema | Einzelheiten |
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| Thema 1 | - 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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| Thema 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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| Thema 3 | - 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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| Thema 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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| Thema 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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CompTIA DataAI Certification Exam DY0-001 Prüfungsfragen mit Lösungen (Q40-Q45):
40. Frage
Which of the following compute delivery models allows packaging of only critical dependencies while developing a reusable asset?
- A. Virtual machines
- B. Thin clients
- C. Edge devices
- D. Containers
Antwort: D
Begründung:
Containers encapsulate just the application and its critical dependencies on a lightweight runtime, making the resulting asset portable and reusable without bundling an entire operating system.
41. Frage
Which of the following is best solved with graph theory?
- A. Optical character recognition
- B. One-armed bandit
- C. Fraud detection
- D. Traveling salesman
Antwort: D
Begründung:
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).
42. Frage
A data scientist is standardizing a large data set that contains website addresses. A specific string inside some of the web addresses needs to be extracted. Which of the following is the best method for extracting the desired string from the text data?
- A. Large language model
- B. Regular expressions
- C. Named-entity recognition
- D. Find and replace
Antwort: B
Begründung:
# Regular expressions (regex) are powerful tools for pattern matching in text. They are ideal for extracting substrings, such as domains, parameters, or specific keywords from URLs or structured text fields.
Why the other options are incorrect:
* B: NER is used to extract named entities (like names, places) - not substrings in structured text.
* C: LLMs are overkill and not efficient for simple string matching tasks.
* D: Find and replace is manual and non-scalable for large data sets.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 6.3:"Regular expressions provide a flexible method to extract patterns and substrings in structured or semi-structured text."
* Data Cleaning Handbook, Chapter 3:"Regex is the most effective tool for parsing text formats like URLs, emails, or custom tags."
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43. Frage
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. Linear
- B. Logarithmic
- C. Polynomial
- D. Exponential
Antwort: D
Begründung:
# 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
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