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CompTIA DY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Specialized Applications of Data Science13%- Specialized Domains
  • 1. Computer Vision
  • 2. Anomaly Detection
  • 3. Natural Language Processing (NLP)
Topic 2: Mathematics and Statistics17%- Statistical Methods and Concepts
  • 1. t-tests, Chi-squared test, ANOVA, Hypothesis testing
  • 2. Gini index, Entropy, Information gain
  • 3. Confusion matrix and Classifier metrics (Accuracy, Recall, Precision, F1, MCC)
  • 4. Regression performance metrics (R2, RMSE, F statistic)
  • 5. Distributions, Skewness, Kurtosis
  • 6. Central limit theorem, Law of large numbers
  • 7. Confidence intervals, p-value, Type I and Type II errors
  • 8. Correlation coefficients (Pearson, Spearman)
- Applied Mathematics
  • 1. Linear Algebra
  • 2. Calculus
  • 3. Probability Density Function (PDF), PMF, CDF
Topic 3: Modeling, Analysis, and Outcomes24%- Model Lifecycle
  • 1. Time Series, Longitudinal Studies, Causal Inference
  • 2. Model Selection and Requirements Validation
  • 3. Performance Evaluation and Benchmarking
- Data Analysis Techniques
  • 1. Exploratory Data Analysis (EDA)
  • 2. Visualization (Box plots, Scatter plots, Heatmaps, Sankey diagrams)
  • 3. Univariate and Multivariate Analysis
- Feature Engineering & Transformation
  • 1. Data transformation (Geocoding, Scaling, Standardization)
  • 2. Feature type identification
  • 3. Handling missingness and Oversampling
Topic 4: Operations and Processes22%- Business & Data Lifecycle
  • 1. Ingestion pipelines, Streaming, Batching, Data lineage
  • 2. Data wrangling, Cleaning, Imputation, Ground truth labeling
  • 3. Data types (Synthetic, Public data)
  • 4. Compliance, KPIs, Requirements gathering
- MLOps & Deployment
  • 1. Deployment environments (Cloud, Hybrid, Edge, On-premises)
  • 2. Workflow models, Version control, Clean code, Unit tests
  • 3. CI/CD, Model deployment, Container orchestration
Topic 5: Machine Learning24%- Supervised & Tree-based Learning
  • 1. Linear/Logistic Regression, KNN, Naive Bayes, Association rules
  • 2. Decision Trees, Random Forest, Boosting, Bagging
- Foundational Concepts
  • 1. Data leakage prevention
  • 2. Loss functions, Bias-variance tradeoff, Regularization
  • 3. Cross-validation, Ensemble models, Hyperparameter tuning
- Deep Learning & Unsupervised Learning
  • 1. Artificial Neural Networks (ANN), Dropout, Batch Normalization
  • 2. Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA, t-SNE)
  • 3. Backpropagation, Deep-learning frameworks, Optimizers

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CompTIA DataAI Certification Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
A data analyst wants to use compression on an analyzed data set and send it to a new destination for further processing. Which of the following issues will most likely occur?

Answer: A

Explanation:
Compression and decompression are CPU‐intensive operations; on large data sets, the extra processing load can significantly spike CPU utilization. Memory, OS support, or library dependencies are far less likely to be the primary bottleneck in a standard compression workflow.


NEW QUESTION # 36
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?

Answer: A

Explanation:
# 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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