TEST DETAILS
Required exam DY0-001
Number of questions Maximum of 90
Types of questions Multiple-choice and performance-based
Length of test 165 minutes
Recommended experience A minimum of 5 years of hands-on experience as a data scientist
Passing score Pass/fail only; no scaled score
EXAM OBJECTIVES (DOMAINS)
The table below lists the domains measured by this examination and the extent to which they are represented.
DOMAIN PERCENTAGE OF EXAMINATION
1.0 Mathematics and Statistics 17%
2.0 Modeling, Analysis, and Outcomes 24%
3.0 Machine Learning 24%
4.0 Operations and Processes 22%
5.0 Specialized Applications of Data Science 13%
Total 100%
About the Exam
The CompTIA DataX certification exam will certify the successful candidate has the knowledge and skills required to:
• Understand and implement data science operations and processes.
• Apply mathematical and statistical methods appropriately and understand the importance of data processing and cleaning, statistical modeling, linear algebra, and calculus concepts.
• Apply machine-learning models and understand deep-learning concepts.
• Utilize appropriate analysis and modeling methods and make justified model recommendations.
• Demonstrate understanding of industry trends and specialized data science applications.
EXAM DEVELOPMENT
CompTIA exams result from subject matter expert workshops and industry-wide survey results regarding the skills and knowledge required of an IT professional.
CompTIA DataX DY0-001 Hardware and Software List
CompTIA has included this sample list of hardware and software to assist candidates as they prepare for the DataX DY0-001 certification exam. This list may also be helpful for training companies that wish to create a lab
component for their training offering. The bulleted lists below each topic are sample lists and are not exhaustive.
Equipment
• Workstations with CUDA-compatible GPU
• GPU on cloud providers
Software
• Linux kernel-based operating systems (preferred)
• Windows operating systems
– Regional packs
– Unicode
– Windows Subsystem for Linux (WSL)
– Docker desktop
• CoderPad
• Python or R
– Relevant packages (visualization, modeling, cleaning, and machine learning)
• Notebook environment/tool set
• Visual Studio Code
• Git
Other
• Large data sets
• Small data sets
• Various types of data sets
QUESTION 1
A data scientist is building an inferential model with a single predictor variable.
A scatter plot of the independent variable against the real-number dependent variable shows a strong relationship
between them. The predictor variable is normally distributed with very few outliers.
Which of the following algorithms is the best fit for this model, given the data scientist wants the model to be easily interpreted?
A. A logistic regression
B. An exponential regression
C. A linear regression
D. A probit regression
Answer: C
Explanation:
QUESTION 2
A data scientist wants to evaluate the performance of various nonlinear models. Which of the following is best suited for this task?
A. AIC
B. Chi-squared test
C. MCC
D. ANOVA
Answer: A
QUESTION 3
Which of the following is the layer that is responsible for the depth in deep learning?
A. Convolution
B. Dropout
C. Pooling
D. Hidden
Answer: D
QUESTION 4
Which of the following modeling tools is appropriate for solving a scheduling problem?
A. One-armed bandit
B. Constrained optimization
C. Decision tree
D. Gradient descent
Answer: B
Explanation:
Scheduling problems require finding the best allocation of resources subject to constraints (e.g., time
slots, resource availability), which is precisely what constrained optimization algorithms are designed to handle.
QUESTION 5
Which of the following environmental changes is most likely to resolve a memory constraint error when running a complex model using distributed computing?
A. Converting an on-premises deployment to a containerized deployment
B. Migrating to a cloud deployment
C. Moving model processing to an edge deployment
D. Adding nodes to a cluster deployment
Answer: D
Explanation:
Increasing the number of nodes in your cluster directly expands the total available memory across
the distributed system, alleviating memory-constraint errors without changing your code or
deployment paradigm. Containerization or edge deployments dont inherently provide more
memory, and migrating to the cloud alone doesnt guarantee additional nodes unless you explicitly scale out.
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Sample Question and Answers
QUESTION 1
A data scientist is building an inferential model with a single predictor variable.
A scatter plot of the independent variable against the real-number dependent variable shows a strong relationship
between them. The predictor variable is normally distributed with very few outliers.
Which of the following algorithms is the best fit for this model, given the data scientist wants the model to be easily interpreted?
A. A logistic regression
B. An exponential regression
C. A linear regression
D. A probit regression
Answer: C
Explanation:
QUESTION 2
A data scientist wants to evaluate the performance of various nonlinear models. Which of the following is best suited for this task?
A. AIC
B. Chi-squared test
C. MCC
D. ANOVA
Answer: A
QUESTION 3
Which of the following is the layer that is responsible for the depth in deep learning?
A. Convolution
B. Dropout
C. Pooling
D. Hidden
Answer: D
QUESTION 4
Which of the following modeling tools is appropriate for solving a scheduling problem?
A. One-armed bandit
B. Constrained optimization
C. Decision tree
D. Gradient descent
Answer: B
Explanation:
Scheduling problems require finding the best allocation of resources subject to constraints (e.g., time
slots, resource availability), which is precisely what constrained optimization algorithms are designed to handle.
QUESTION 5
Which of the following environmental changes is most likely to resolve a memory constraint error when running a complex model using distributed computing?
A. Converting an on-premises deployment to a containerized deployment
B. Migrating to a cloud deployment
C. Moving model processing to an edge deployment
D. Adding nodes to a cluster deployment
Answer: D
Explanation:
Increasing the number of nodes in your cluster directly expands the total available memory across
the distributed system, alleviating memory-constraint errors without changing your code or
deployment paradigm. Containerization or edge deployments dont inherently provide more
memory, and migrating to the cloud alone doesnt guarantee additional nodes unless you explicitly scale out.
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