CompTIA DataX (DY0-001) Practice Exam

The CompTIA DataX (DY0-001) is a professional certification that shows foundational knowledge and skills in data management and analytics. It shows that a person can work with data, understand data processes, and extract meaningful insights to support business decisions. This certification is ideal for data analysts, IT professionals, and anyone starting a career in data management or business intelligence.

Recognized globally, the DY0-001 certification helps professionals stand out in roles that involve handling and analyzing data. By earning this credential, individuals demonstrate the ability to manage datasets, understand data pipelines, and ensure data quality. Organizations benefit from certified professionals who can make informed, data-driven decisions, improve operations, and enhance business performance.

 

Who should take the Exam?

This exam is ideal for:

• Data Scientists
• Machine Learning Engineers
• Quantitative Analysts
• Operations Research Analysts
• Business Intelligence Analysts
• Data Engineers

 

Skills Required

• Mathematics and Statistics
• Modeling and Analysis
• Machine Learning
• Data Science Operations
• Specialized Applications

 

Knowledge Gained

• Statistical Methods and Probability
• Exploratory Data Analysis (EDA)
• Supervised and Unsupervised Learning
• Deep Learning Concepts
• Data Wrangling and Pipeline Management
• Deployment and Monitoring of Models
• Natural Language Processing (NLP)
• Computer Vision Techniques

Course Outline

The CompTIA DataX  DY0-001 Exam covers the following topics - 
 

Domain 1 - Mathematics and statistics (17%)

• Statistical methods: applying t-tests, chi-squared tests, analysis of variance (ANOVA), hypothesis testing, regression metrics, gini index, entropy, p-value, receiver operating characteristic/area under the curve (ROC/AUC), akaike information criterion/bayesian information criterion (AIC/BIC), and confusion matrix.
• Probability and modeling: explaining distributions, skewness, kurtosis, heteroskedasticity, probability density function (PDF), probability mass function (PMF), cumulative distribution function (CDF), missingness, oversampling, and stratification.
• Linear algebra and calculus: understanding rank, eigenvalues, matrix operations, distance metrics, partial derivatives, chain rule, and logarithms.
• Temporal models: comparing time series, survival analysis, and causal inference.

 

Domain 2 - Modeling, analysis, and outcomes (24%)

• EDA methods: using exploratory data analysis (EDA) techniques like univariate and multivariate analysis, charts, graphs, and feature identification.
• Data issues: analyzing sparse data, non-linearity, seasonality, granularity, and outliers.
• Data enrichment: applying feature engineering, scaling, geocoding, and data transformation.
• Model iteration: conducting design, evaluation, selection, and validation.
• Results communication: creating visualizations, selecting data, avoiding deceptive charts, and ensuring accessibility.

 

Domain 3 - Machine learning (24%)

• Foundational concepts: applying loss functions, bias-variance tradeoff, regularization, cross-validation, ensemble models, hyperparameter tuning, and data leakage.
• Supervised learning: applying linear regression, logistic regression, k-nearest neighbors (KNN), naive bayes, and association rules.
• Tree-based learning: applying decision trees, random forest, boosting, and bootstrap aggregation (bagging).
• Deep learning: explaining artificial neural networks (ANN), dropout, batch normalization, backpropagation, and deep-learning frameworks.
• Unsupervised learning: explaining clustering, dimensionality reduction, and singular value decomposition (SVD).

 

Domain 4 - Operations and processes (22%)

• Business functions: explaining compliance, key performance indicators (KPIs), and requirements gathering.
• Data types: explaining generated, synthetic, and public data.
• Data ingestion: understanding pipelines, streaming, batching, and data lineage.
• Data wrangling: implementing cleaning, merging, imputation, and ground truth labeling.
• Data science life cycle: applying workflow models, version control, clean code, and unit tests.
• DevOps and MLOps: explaining continuous integration/continuous deployment (CI/CD), model deployment, container orchestration, and performance monitoring.
• Deployment environments: comparing containerization, cloud, hybrid, edge, and on-premises deployment.

 

Domain 5 - Specialized applications of data science (13%)

• Optimization: comparing constrained and unconstrained optimization.
• NLP concepts: explaining natural language processing (NLP) techniques like tokenization, embeddings, term frequency-inverse document frequency (TF-IDF), topic modeling, and NLP applications.
• Computer vision: explaining optical character recognition (OCR), object detection, tracking, and data augmentation.
• Other applications: explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.