ISACA Data Science Fundamentals Certification Sample Questions

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ISACA Data Science Fundamentals Sample Questions:

01. Which scaling techniques are appropriate for preparing features in data preprocessing?
(Choose Two)
a) Min-Max Scaling
b) One-Hot Encoding
c) Standardization
d) Tokenization
02. CRISP-DM stands for what in the context of a data science project?
a) Cross-Industry Standard Process for Data Management
b) Cross-Industry Standard Practice for Data Mining
c) Cross-Industry Standard Process for Data Mining
d) Cross-Industry Systematic Protocol for Data Management
03. What type of machine learning algorithm would be best suited for creating a system that suggests products based on customer buying history?
a) Unsupervised learning
b) Supervised learning
c) Reinforcement learning
d) Semi-supervised learning
04. What are the benefits of using ensemble methods in machine learning?
(Choose Three)
a) Reducing variance
b) Reducing bias
c) Improving prediction confidence
d) Simplifying models
05. Which SQL statement is used to remove duplicates from a result set?
06. In data visualization, what does a 'heatmap' typically represent?
a) Geographical distributions
b) Trends over time
c) Relationships between two variables
d) Data density through variations in coloring
07. What is the main purpose of data lifecycle management (DLM)?
a) To define processes for resolving data breaches
b) To manage data from creation to deletion
c) To increase data transparency only
d) To ensure data is never archived
08. Which of the following is a common method to ensure data security in management practices?
a) Regular data audits
b) Frequent data duplication
c) Periodic system shutdowns
d) Reduced data access speeds
09. In the context of data science, what is 'model validation'?
a) Documenting the steps involved in building the model
b) The process of ensuring a model operates within certain parameters
c) Testing a model on new data to check its performance
d) Integrating the model with existing databases
10. Which algorithms are typically used for unsupervised learning tasks?
(Choose Two)
a) Decision Trees
b) K-means Clustering
c) Support Vector Machines
d) Principal Component Analysis


Question: 01
Answer: a, c
Question: 02
Answer: c
Question: 03
Answer: b
Question: 04
Answer: a, b, c
Question: 05
Answer: a
Question: 06
Answer: d
Question: 07
Answer: b
Question: 08
Answer: a
Question: 09
Answer: c
Question: 10
Answer: b, d

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