SDS exam dumps

DASCA SDS Value Package

(Include: PDF + Desktop Test Engine + Online Test Engine)

  • Exam Code: SDS
  • Exam Name: Senior Data Scientist
  • No. of Questions: 87 Questions and Answers
  • Updated: Aug 18, 2026

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SDS Desktop Test Engine

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  • Total Questions: 87
  • Updated on: Aug 18, 2026
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SDS PDF Practice Q&A's

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  • Total Questions: 87
  • Updated on: Aug 18, 2026
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  • Total Questions: 87
  • Updated on: Aug 18, 2026
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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

1. Which of the following is a DevOps Practice?

A) Continuous delivery
B) Continuous integration
C) All of the above
D) Continuous build


2. Machine learning can be used in:

A) Pattern and image recognition
B) Fraud detection
C) Web search results
D) Real-time ads on web pages and mobile devices
E) All of the above


3. Exploratory analytic algorithms help the Data Science team to better:

A) Understand patterns in the data
B) Gain a high-level understanding of relationships
C) Both A and B
D) Understand the data content
E) All of the above


4. Semi-structured data does NOT include:

A) Database system
B) File systems
C) Schema-full data
D) Scientific data


5. Which of the following is TRUE for data lake?

A) None of the above
B) The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
C) The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
D) The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
E) The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive


Solutions:

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

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