DELL TECHNOLOGIESDell Data Science Foundations
The Dell Data Science Foundations certification validates your understanding of the data analytics lifecycle, from framing business drivers and exploring data with R to applying advanced analytics methods and operationalizing results. It is designed for systems engineers and technical consultants who support data science initiatives. Earning it demonstrates that you can speak the language of data science and contribute to analytics projects with confidence.
695 practice questions · Updated 2026-07-30
6Domains
27Objectives
176Concepts
695Questions
DATA-SCIENCE-FOUNDATIONS Curriculum
Every domain, objective, and concept the DATA-SCIENCE-FOUNDATIONS exam measures.
- Definition of Big Data
- The Five V's of Big Data
- Volume
- Velocity
- Variety
- Veracity
- Value
- Identify business drivers for Big Data analytics
- Recognize the role of data science in business
- Understand the relationship between Big Data and data science
- Describe the impact of Big Data on business strategy
- Purpose of the data analytics lifecycle
- Sequence of lifecycle phases
- Role of each phase
- Discovery phase overview
- Discovery activities
- Roles in Discovery
- Discovery deliverables
- Data preparation phase overview
- Key activities in data preparation
- Roles involved in data preparation
- Model planning phase overview
- Key activities in model planning
- Roles involved in model planning
- Inputs and outputs of model planning
- Model building phase overview
- Key activities in model building
- Roles involved in model building
- Basic R data structures
- Reading data into R
- Initial data inspection
- Descriptive statistics
- Handling missing values
- Data subsetting and filtering
- Basic data visualization
- Descriptive Statistics
- Distribution Shape
- Correlation Measures
- Data Visualization Principles
- Visualization Examples
- Hypothesis testing theory
- Hypothesis testing process
- Interpreting hypothesis test results
- Hypothesis testing in model evaluation
- K-means algorithm fundamentals
- Choosing the number of clusters (k)
- Distance metrics in K-means
- Sensitivity to initialization
- Convergence and stopping criteria
- Interpretation of K-means results
- Limitations and assumptions of K-means
- Preprocessing for K-means
- Association rules fundamentals
- Support, confidence, and lift
- Apriori algorithm
- Rule interpretation and evaluation
- Linear regression fundamentals
- Ordinary least squares estimation
- Model assumptions
- Coefficient interpretation
- Goodness-of-fit measures
- Hypothesis testing for coefficients
- Confidence intervals for predictions
- Residual analysis
- Multicollinearity
- Categorical predictors and dummy variables
- Model selection and validation
- Limitations and alternatives
- Logistic Regression Fundamentals
- Sigmoid Function and Odds
- Model Equation and Interpretation
- Maximum Likelihood Estimation
- Model Evaluation Metrics
- ROC and AUC
- Assumptions and Diagnostics
- Applications and Limitations
- Decision tree structure
- Splitting criteria
- Tree construction algorithms
- Pruning techniques
- Handling different data types
- Interpreting decision tree results
- Advantages and limitations
- Time Series Components
- Stationarity
- Autocorrelation and Partial Autocorrelation
- Smoothing Methods
- ARIMA Models
- Seasonal Decomposition
- Forecast Evaluation
- Model Selection and Diagnostics
- Text Analytics Overview
- Text Preprocessing
- Bag-of-Words and TF-IDF
- Topic Modeling
- Sentiment Analysis
- Text Classification
- Named Entity Recognition
- Interpretation of Text Analytics Results
- Big Data Volume Challenge
- Big Data Velocity Challenge
- Big Data Variety Challenge
- Big Data Veracity Challenge
- Big Data Value Challenge
- Big Data Variability Challenge
- Big Data Complexity Challenge
- Technological Limitations of Traditional Systems
- Scalability Requirements
- Data Storage and Management Challenges
- Data Processing and Analysis Challenges
- Data Integration Challenges
- Data Security and Privacy Challenges
- Data Governance Challenges
- Real-Time Processing Challenges
- Distributed Computing Challenges
- Data Visualization Challenges
- MapReduce paradigm
- Hadoop ecosystem
- Hadoop architecture
- Data flow in MapReduce
- HDFS characteristics
- Use cases of Hadoop
- In-database analytics overview
- SQL basics for analytics
- SQL joins and subqueries
- SQL window functions
- SQL for data preparation
- Integration with analytics tools
- Window Functions
- Syntax and Usage of Window Functions
- Ordered Aggregates
- MADlib Overview
- MADlib Functions and Usage
- Effective Communication of Findings
- Storytelling with Data
- Operationalizing Analytics Projects
- Monitoring and Maintenance of Analytics Solutions
- Audience Analysis
- Tailoring Content
- Visual Design Principles
- Storytelling with Data
- Delivery and Engagement
- Define data visualization goals
- Select appropriate chart types
- Apply design principles
- Ensure data accuracy and integrity
- Iterate and gather feedback
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for DATA-SCIENCE-FOUNDATIONS, so none is invented.