DASCA SDS Exam Details & Actual Exam Questions

  • Exam Code/Number: SDS
  • Exam Name/Title: Senior Data Scientist
  • Certification Provider: DASCA
  • Corresponding Certification: DASCA Data Scientist
  • Exam Questions: 87
  • Updated On: Jul,18 2026
  • Certification Level: Senior/Expert

DASCA Senior Data Scientist Exam Questions

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DASCA SDS Exam Overview:

Certification Vendor:DASCA
Exam Name:DASCA Senior Data Scientist Certification
Exam Number:SDS
Certificate Validity Period:3 Years
Passing Score:65%
Real Exam Qty:100
Exam Price:USD 650
Related Certifications:DASCA Certified Data Scientist (CDS)
DASCA Principal Data Scientist (PDS)
Available Languages:English
Exam Duration:100 minutes
Exam Format:Multiple Choice, Multiple Response, Scenario-based Questions
Sample Questions:DASCA SDS Sample Questions
Exam Way:Online proctored or at authorized testing centers
Pre Condition:Recommended: Bachelor's degree in a quantitative field, prior data science experience or DASCA CDS certification
Official Syllabus URL:https://www.dasca.org/data-science-certifications/senior-data-scientist-sds

DASCA SDS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Foundations of Data Science15-20%- Data Science Lifecycle
  • 1. Deployment and Monitoring
  • 2. Problem Formulation
  • 3. Data Collection and Preparation
  • 4. Model Building and Evaluation
- Data Science Ethics
  • 1. Privacy and Confidentiality
  • 2. Bias and Fairness
  • 3. Transparency and Explainability
Topic 2: Advanced Statistical Modeling20-25%- Statistical Inference
  • 1. Hypothesis Testing
  • 2. Bayesian Methods
  • 3. Confidence Intervals
- Regression Analysis
  • 1. Generalized Linear Models
  • 2. Regularization Techniques
  • 3. Linear and Logistic Regression
Topic 3: Big Data Engineering15-20%- Distributed Computing
  • 1. Hadoop Ecosystem
  • 2. Data Pipelines
  • 3. Apache Spark
- Cloud Computing for Data Science
  • 1. AWS/Azure/GCP Services
  • 2. Serverless Architectures
  • 3. Containerization
Topic 4: Machine Learning at Scale25-30%- Deep Learning Fundamentals
  • 1. Neural Networks
  • 2. Convolutional Networks
  • 3. Recurrent Networks
- Supervised Learning
  • 1. Time Series Forecasting
  • 2. Ensemble Methods
  • 3. Support Vector Machines
- Unsupervised Learning
  • 1. Clustering Algorithms
  • 2. Dimensionality Reduction
  • 3. Anomaly Detection
Topic 5: Data Visualization and Communication10-15%- Visualization Principles
  • 1. Dashboard Design
  • 2. Storytelling with Data
- Tools and Technologies
  • 1. Python Visualization Libraries
  • 2. Tableau/Power BI


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