DASCA SDS Exam : Senior Data Scientist

  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
  • Updated: Aug 24, 2026
  • Q & A: 87 Questions and Answers

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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

1. Designing an algorithm to play chess is usually an example of which type of machine learning?

A) Supervised learning
B) Clustering
C) Reinforcement learning
D) Pattern density


2. Which of the following is NOT a valid section of Big Data Strategy document?

A) Key decisions
B) Key business initiatives
C) Key business entities
D) Business strategy
E) Business decisions


3. What is the agenda of discussion at a "stand up" meeting of an Agile team?

A) What they accomplished the previous day
B) What they are planning to do today
C) All of the above
D) Both A and B
E) Any roadblocks they are running into


4. Maximum Likelihood Estimation (MLE) is a way to frame:

A) Small class of problems in Data Science
B) Large class of problems in HDFS
C) Large class of problems in Data Science
D) Both A and C
E) Small class of problems in HDFS


5. What is TRUE for "rehashing"?

A) Key/value pairs from the original table can be inserted into the new, larger one
B) All of the above
C) Both A and B
D) It requires a new hash function, which maps values into a larger range of integers
E) Allocate a new, larger hash table in memory


Solutions:

Question # 1
Answer: C
Question # 2
Answer: E
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: B

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