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CompTIA DY0-001 exam : CompTIA DataAI Certification Exam

DY0-001 Exam Simulator
  • Exam Code: DY0-001
  • Exam Name: CompTIA DataAI Certification Exam
  • Updated: Oct 15, 2026
  • Q & A: 85 Questions and Answers
  • CompTIA DY0-001 Q&A - in .pdf

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CompTIA DY0-001 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • 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.

Reference: https://www.comptia.org/en-us/certifications/dataai/

CompTIA DataAI Certification Sample Questions:

Question #1

Given matrix

Which of the following is AT?

  • A.
  • B.
  • C.
  • D.
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Correct Answer: C  🗳️

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Question #2

Which of the following types of machine learning is a GPU most commonly used for?

  • A. Tree-based
  • B. Clustering
  • C. Natural language processing
  • D. Deep learning/neural networks
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

Which of the following does k represent in the k-means model?

  • A. Number of data splits
  • B. Number of model tests
  • C. Distance between features
  • D. Number of clusters
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

A data scientist is developing a model to predict the outcome of a vote for a national mascot. The choice is between tigers and lions. The full data set represents feedback from individuals representing 17 professions and 12 different locations. The following rank aggregation represents 80% of the data set:

Which of the following is the most likely concern about the model's ability to predict the outcome of the vote?

  • A. In-sample data
  • B. Interpolated data
  • C. Out-of-sample data
  • D. Extrapolated data
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

Which of the following explains back propagation?

  • A. The passage of convolutions backward through a neural network to update weights and biases
  • B. The passage of nodes backward through a neural network to update weights and biases
  • C. The passage of errors backward through a neural network to update weights and biases
  • D. The passage of accuracy backward through a neural network to update weights and biases
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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