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DASCA SDS Exam : Senior Data Scientist

SDS actual test
  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
  • Updated: Aug 17, 2026
  • Q & A: 87 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About DASCA SDS Exam

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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

1. Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?

A) Scatter Plot
B) None of the above
C) Histogram
D) Box Plots
E) Trend Analysis


2. Which of the following is an example of NLP?

A) Using Twitter to assess public sentiment
B) Finding which text documents are about similar topics
C) All of the above
D) Flagging e-mails as spam
E) Both A and C


3. Which of the following is NOT an example of the applications of neural networks?

A) Image compression
B) Stock market prediction
C) Traveling salesman's problem
D) None of the above
E) Character recognition


4. Spark should be used when:

A) None of the above
B) Both A and B
C) Data is not massive
D) Data is massive


5. In regression, the principle of machine learning is used to optimize the parameters to:

A) Minimize the approximation error
B) None of the above
C) Both A and B
D) Calculate the closest possible outcomes


Solutions:

Question # 1
Answer: D
Question # 2
Answer: C
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: C

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