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DASCA SDS dumps - in .pdf

SDS pdf
  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
  • Updated: Aug 04, 2026
  • Q & A: 87 Questions and Answers
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  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
  • Updated: Aug 04, 2026
  • Q & A: 87 Questions and Answers
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  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
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SDS exam dumps

DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

1. Which of the following is correct about customer lifetime value (CLTV)?
i. Most organizations determine the current customer lifetime value (CLTV) based on historic sales over past
12 to 18 months
ii. The goal of the CLTV score is to help marketing and store personnel to determine the "value" of a customer

A) Only i
B) Only ii
C) Both i and ii


2. Which of the following architectural techniques is used for parallel processing?

A) The Superscalar Technique
B) The SuperVector Technique
C) Both B and C
D) Both A and B
E) Very Long Instruction Words (VLIW) Technique


3. Which of the following is TRUE about monetization exercise?

A) The monetization exercise provides an opportunity to uncover new product and/or service opportunities
B) Both B and C
C) The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
D) All of the above
E) The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service


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

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


5. JSON takes hierarchical data structures and serializes them into:

A) Plain text format
B) Both A and B
C) Plain string format
D) Any desired format
E) None of the above


Solutions:

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

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