CompTIA DataAI (DY0-001) Certification exam

Course Overview

The CompTIA DataAI (DY0-001) Training and Certification program is designed for experienced data professionals, AI engineers, machine learning practitioners, and analytics specialists who want to validate advanced skills in data science, artificial intelligence, machine learning, MLOps, and advanced analytics.

Previously known as CompTIA DataX, CompTIA officially rebranded this certification to DataAI, while keeping the same DY0-001 exam code. This expert-level certification focuses on the complete AI and data science lifecycle—from mathematical foundations and data preparation to model development, deployment, and specialized AI applications.

Offered by Linux Training Center, Coimbatore, this course aligns with the latest CompTIA DataAI (DY0-001) exam objectives and provides advanced hands-on training in machine learning, deep learning, statistical modeling, data engineering, and production AI systems.

Who Should Enroll?

  • Data scientists and senior data analysts
  • Machine learning engineers
  • AI engineers and research professionals
  • Data engineers managing ML pipelines
  • MLOps engineers handling model deployment
  • Cloud AI professionals working with enterprise AI workloads
  • IT professionals transitioning into advanced AI and data science roles

Why This Course Stands Out

  • Complete coverage of CompTIA DataAI DY0-001 exam objectives
  • Advanced hands-on labs with real-world AI and ML workflows
  • Strong focus on practical model building and deployment
  • Real-world case studies involving AI and data science applications
  • Industry-aligned curriculum for modern AI and data roles
  • Certification-focused mock exams and performance-based assessments
  • Practical exposure to end-to-end AI lifecycle management

Exam Details

  • Exam Code: DY0-001
  • Certification Name: CompTIA DataAI
  • Question Format: Multiple-choice and performance-based questions
  • Number of Questions: Up to 90
  • Exam Duration: 165 minutes
  • Recommended Experience: Minimum 5 years of hands-on experience in data science or related roles
  • Scoring: Pass/Fail model

What You’ll Learn (DY0-001 Exam Objectives)

1. Mathematics and Statistics (17%)

  • Statistical analysis and probability concepts
  • Hypothesis testing and regression analysis
  • Data distributions and sampling techniques
  • Linear algebra fundamentals
  • Basic calculus concepts for machine learning

2. Modeling, Analysis, and Outcomes (24%)

  • Data preprocessing and feature engineering
  • Model selection and validation
  • Performance evaluation metrics
  • Hyperparameter tuning
  • Model interpretation and business outcomes

3. Machine Learning (24%)

  • Supervised learning models
  • Unsupervised learning techniques
  • Tree-based algorithms
  • Deep learning fundamentals
  • Neural networks and model optimization

4. Operations and Processes (22%)

  • MLOps and AI workflows
  • Model deployment strategies
  • Data pipelines and automation
  • Monitoring and performance tracking
  • CI/CD for AI systems

5. Specialized Applications of Data Science (13%)

  • Natural Language Processing (NLP)
  • Computer Vision
  • Reinforcement Learning
  • Generative AI concepts
  • Industry-specific AI applications

Career Roles You Can Pursue

  • Data Scientist
  • Machine Learning Engineer
  • AI Engineer
  • Data Engineer
  • MLOps Engineer
  • Research Scientist
  • AI Solutions Architect
  • Advanced Analytics Specialist

Why Choose Linux Training Center, Coimbatore?

  • Expert trainers with AI, ML, and data science experience
  • Advanced practical labs with enterprise AI tools
  • Hands-on projects covering real-world AI applications
  • Flexible weekday and weekend training schedules
  • Comprehensive study materials and lab access
  • Mock exams and certification-focused preparation
  • Career guidance and placement assistance
  • Post-training mentorship until certification completion

Become a DataAI Expert

Advance your career with CompTIA DataAI (DY0-001) certification training. Master advanced data science, machine learning, MLOps, and AI technologies to excel in the rapidly growing world of artificial intelligence and data-driven innovation.

CompTIA DataAI (DY0-001) Certification exam

Modules

Mathematics and Statistics
  • Given a scenario, apply the appropriate statistical method or concept.
  • t-tests
  • Chi-squared test
  • Analysis of variance (ANOVA)
  • Hypothesis testing
  • Confidence intervals
  • Regression performance metrics
  • R2
  • Adjusted R2
  • Root mean square error (RMSE)
  • F statistic
  • Gini index
  • Entropy
  • Information gain
  • p value
  • Type I and Type II errors
  • Receiver operating characteristic/area under the curve (ROC/AUC)
  • Akaike information criterion/Bayesian information criterion (AIC/BIC)
  • Correlation coefficients
  • Pearson correlation
  • Spearman correlation
  • Confusion matrix
  • Classifier performance metrics
  • Accuracy
  • Recall
  • Precision
  • F1 score
  • Matthews Correlation Coefficient (MCC)
  • Central limit theorem
  • Law of large numbers
    • Probability and Synthetic Modeling Concepts
  • Distributions
  • Normal
  • Uniform
  • Poisson
  • t
  • Binomial
  • Power law
  • Skewness
  • Kurtosis
  • Heteroskedasticity vs. homoskedasticity
  • Probability density function (PDF)
  • Probability mass function (PMF)
  • Cumulative distribution function (CDF)
  • Probability
  • Monte Carlo simulation
  • Bootstrapping
  • Bayes’ rule
  • Expected value
  • Types of missingness
  • Missing at random
  • Missing completely at random
  • Not missing at random
  • Oversampling
  • Stratification
    • Linear Algebra and Basic Calculus
  • Linear algebra
  • Rank
  • Span
  • Trace
  • Eigenvalues/eigenvectors
  • Basis vector
  • Identity matrix
  • Matrix and vector operations
  • Matrix multiplication
  • Matrix transposition
  • Matrix inversion
  • Matrix decomposition
  • Distance metrics
  • Euclidean
  • Radial
  • Manhattan
  • Cosine
  • Calculus
  • Partial derivatives
  • Chain rule
  • Exponentials
  • Logarithms
    • Temporal Models
  • Time series
  • Autoregressive (AR)
  • Moving average (MA)
  • Autoregressive integrated moving average (ARIMA)
  • Longitudinal studies
  • Survival analysis
  • Parametric
  • Non-parametric
  • Causal inference
  • Directed acyclic graphs (DAGs)
  • Difference-in-differences
  • A/B testing of treatment effects
  • Randomized controlled trials
    • Modeling, Analysis, and Outcomes
  • Exploratory data analysis (EDA)
  • Univariate analysis
  • Multivariate analysis
  • Charts and graphs
  • Bar plot
  • Scatter plot
  • Box and whisker plot
  • Line plot
  • Violin plot
  • Heat map
  • Correlation plot
  • Histogram
  • Sankey diagram
  • Quartile-Quartile (Q-Q) plot
  • Density plot
  • Scatter plot matrix
  • Feature type identification
  • Categorical variables
  • Discrete variables
  • Continuous variables
  • Ordinal variables
  • Nominal variables
  • Binary variables
    • Common Data Issues
  • Sparse data
  • Sparse matrix
  • Sparse vectors
  • Non-linearity
  • Non-stationarity
  • Lagged observations
  • Difference observations
  • Multicollinearity
  • Seasonality
  • Granularity misalignment
  • Insufficient features
  • Multivariate outliers
    • Machine Learning
  • Foundational machine-learning concepts
  • Loss function
  • Variance minimization
  • Bias-variance tradeoff
  • Overfitting
  • Underfitting
  • Variable/feature selection
  • Feature importance
  • Multicollinearity
  • Correlation matrix
  • Variance inflation factor (VIF)
  • Class imbalance and mitigations
  • Oversampling the minority class
  • Undersampling the majority class
  • Synthetic minority oversampling technique (SMOTE)
  • Regularization
  • Cross-validation
  • k-fold
  • The curse of dimensionality
  • Occam’s razor/law of parsimony
  • In sample vs. out of sample
  • Interpolation vs. extrapolation
  • Ensemble models
  • Hyperparameter tuning
  • Grid search
  • Random search
  • Classifiers
  • Binary classifiers
  • Multiclass (multinomial) classifiers
  • Recommender systems
  • Collaborative filtering
  • Alternating least squares (ALS)
  • Similarity-based
  • Regressors
  • Embeddings
  • Post hoc model explainability
  • Global explanations
  • Local explanations
  • Interpretable models
  • Model drift causes
  • Data drift
  • Concept drift
  • Data leakage
  • Transfer learning
  • Cold start problem
    • Supervised Machine Learning
  • Linear regression models
  • Ordinary least squares (OLS)
  • Weighted least squares
  • Ridge
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic net
  • Logistic regression models
  • Probit
  • Logit
  • Linear discriminant analysis
  • Quadratic discriminant analysis (QDA)
  • Association rules
  • Confidence
  • Lift
  • Reinforcement
  • Support
  • Naive Bayes
    • Tree-Based Machine Learning
  • Decision trees
  • Random forest
  • Boosting
  • Gradient boosting
  • XGBoost
  • Bootstrap aggregation (bagging)
    • Deep Learning
  • Artificial neural network architecture
  • Perceptron
  • Artificial neuron
  • Multilayer perceptron
  • Activation functions
  • Rectified linear unit (ReLU)
  • Sigmoid
  • Tanh
  • Softmax
  • Layer types
  • Input
  • Hidden
  • Pooling
  • Output
  • Dropout
  • Batch normalization
  • Early stopping
  • Schedulers
  • Back propagation
  • One-shot learning
  • Zero-shot learning
  • Few-shot learning
  • PyTorch
  • TensorFlow/Keras
  • AutoML
  • Optimizers
  • Adam optimizer
  • Momentum
  • RMSprop
  • Stochastic gradient descent
  • Mini-batch
  • CNN
  • RNN
  • LSTM
  • GANs
  • Autoencoders
  • Transformers
    • Unsupervised Machine Learning
  • Clustering
  • k-means
  • Silhouette score
  • Elbow method
  • Hierarchical
  • DBSCAN
  • Dimensionality reduction
  • PCA
  • t-SNE
  • UMAP
  • k-nearest neighbors (KNN)
  • Singular value decomposition (SVD)
    • Operations and Processes
  • Data science in business functions
  • Compliance, security, and privacy
  • Personally identifiable information (PII)
  • Proprietary data
  • Anonymizing sensitive data
  • Data obfuscation
  • Data use regulations
  • Measures, metrics, and KPIs
  • Requirements gathering
  • Cost-benefit analysis
  • Business solution mapping
    • Data Acquisition and Storage
  • Generated data
  • Survey
  • Administrative
  • Sensor
  • Transactional
  • Experimental
  • Data-generating process
  • Synthetic data
  • Costs and benefits
  • Creation process
  • Limitations
  • Sampling
  • Commercial/public data
  • Availability
  • Licensing
  • Restrictions
  • Infrastructure requirements
  • Resource sizing
  • GPU/TPU
  • Data formats
  • CSV
  • JSON
  • Parquet
  • Compressed format
  • Structured storage
  • Semi-structured storage
  • Unstructured storage
  • Streaming
  • Batching
  • Pipeline implementation
  • Orchestration/automation
  • Persistence
  • Refresh cycles
  • Archiving
  • Data lineage
    • Data Wrangling
  • Merging/combining
  • Defining keys
  • Data matching
  • Match rates
  • Fuzzy join
  • Observation tracking
  • Union
  • Intersection
  • Types of joins
  • Cleaning
  • Date/time standardization
  • Regular expressions
  • Deduplication
  • Unit conversion/standardization
  • Missing codes
  • Data errors
  • Idiosyncratic
  • Systematic
  • Outliers
  • Identification
  • Winsorization/cut points
  • Error vs. valid data point
  • Data flattening
  • XML
  • JSON
  • Imputation types
  • Ground truth labeling
    • Data Science Lifecycle Best Practices
  • CRISP-DM
  • DAMA
  • Version control
  • Code
  • Data
  • Hyperparameters
  • Models
  • IDE
  • Dependency licensing
  • API access
  • Data access and retrieval
  • Model endpoint/model services
  • Process documentation
  • Markdown
  • Docstring
  • Code commenting
  • Reference data and documentation
  • Clean code methods
  • Unit test writing
    • DevOps and MLOps
  • Data replication
  • CI/CD pipelines
  • Model deployment
  • Container orchestration
  • Virtualization
  • Code isolation
  • Model performance monitoring
  • Model validation
  • Online
  • Offline
  • Model A/B testing
    • Deployment Environments
  • Containerization
  • Cloud deployment
  • Cluster deployment
  • Hybrid deployment
  • Edge deployment
  • On-premises deployment
    • Specialized Applications of Data Science
  • Optimization concepts
  • Constrained optimization
  • Network topology
  • Traveling salesman
  • Scheduling
  • Linear solvers
  • Simplex method
  • Non-linear solvers
  • Pricing
  • Resource allocation
  • Bundling
  • Boundary cases
  • Unconstrained optimization
  • One-armed bandit
  • Multi-armed bandit
  • Finding local maxima or minima
    • NLP Concepts
  • Tokenization
  • Bag of words
  • Word embeddings
  • n-grams
  • TF-IDF
  • Document term matrix
  • Edit distance
  • Large language models
  • Word2vec
  • GloVe
  • Lemmatization
  • Stop words
  • Augmenters
  • String indexing
  • Stemming
  • POS tagging
  • Topic modeling
  • Latent Dirichlet Allocation
  • Sentiment analysis
  • NER
  • Text generation
  • Speech recognition
  • Text summarization
  • NLU
  • NLG
    • Computer Vision and Other Applications
  • OCR
  • Object segmentation
  • Object detection
  • Tracking
  • Sensor fusion
  • Data augmentation
  • Rotation
  • Occlusion
  • Noise
  • Flipping
  • Scaling
  • Masking
  • Cropping
  • Graph analysis
  • Heuristics
  • Greedy algorithms
  • Reinforcement learning
  • Event detection
  • Fraud detection
  • Anomaly detection
  • Multimodal machine learning
  • Optimization for edge computing
  • Signal processing

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