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DASCA SDS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Foundations of Data Science	15-20%	- Data Science Ethics 1. Privacy and Confidentiality 2. Bias and Fairness 3. Transparency and Explainability - Data Science Lifecycle 1. Deployment and Monitoring 2. Problem Formulation 3. Data Collection and Preparation 4. Model Building and Evaluation
Topic 2: Big Data Engineering	15-20%	- 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 3: Machine Learning at Scale	25-30%	- Supervised Learning 1. Support Vector Machines 2. Ensemble Methods 3. Time Series Forecasting - Deep Learning Fundamentals 1. Neural Networks 2. Convolutional Networks 3. Recurrent Networks - Unsupervised Learning 1. Dimensionality Reduction 2. Clustering Algorithms 3. Anomaly Detection
Topic 4: Advanced Statistical Modeling	20-25%	- Statistical Inference 1. Bayesian Methods 2. Confidence Intervals 3. Hypothesis Testing - Regression Analysis 1. Regularization Techniques 2. Generalized Linear Models 3. Linear and Logistic Regression
Topic 5: Data Visualization and Communication	10-15%	- Tools and Technologies 1. Python Visualization Libraries 2. Tableau/Power BI - Visualization Principles 1. Storytelling with Data 2. Dashboard Design

DASCA Senior Data Scientist Sample Questions:

1. Which of these are open-source column-oriented databases?

A) All of the above
B) HBase
C) Accumulo
D) Both A and B
E) Cassandra

2. What is Scrumban?

A) It is Scrum
B) It combines the principles of Scrum and Kanban into a pull-based system
C) It combines the principles of Scrum and Kanban into a push-based system
D) It is Kanban

3. Which of the following is NOT used to implement Agile?

A) Kanban
B) Scrum
C) Extreme Programming (XP)
D) Six Sigma

4. IoT is built on:

A) Both A and B
B) Cloud Computing
C) Networks of data gathering devices
D) None of the above

5. Which of the following is the common evolutionary algorithm of neural networks?

A) All of the above
B) Genetic algorithm
C) Genetic programming
D) Evolution strategy

Solutions:

Question # 1
Answer: A

Question # 2
Answer: B

Question # 3
Answer: D

Question # 4
Answer: A

Question # 5
Answer: A