Updated CompTIA DY0-001 Dumps – Check Free DY0-001 Exam Dumps (2026) [Q15-Q37]

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Updated CompTIA DY0-001 Dumps – Check Free DY0-001 Exam Dumps (2026)

Updated DY0-001 exam with CompTIA Real Exam Questions

CompTIA DY0-001 Exam Overview:

Certification Vendor:	CompTIA
Exam Name:	CompTIA DataAI Certification Exam
Exam Number:	DY0-001
Exam Format:	Multiple Choice, Performance-Based
Available Languages:	English, Japanese
Passing Score:	Pass/Fail (No scaled score)
Exam Price:	$529 USD
Related Certifications:	CompTIA DataAI (formerly DataX)
Certificate Validity Period:	Usually 3 years
Exam Duration:	165 minutes
Real Exam Qty:	Up to 90
Sample Questions:	CompTIA DY0-001 Sample Questions
Exam Way:	Available at Pearson VUE testing centers or via online proctoring (OnVUE).
Pre Condition:	5+ years of experience in data science or a similar role recommended.
Official Syllabus URL:	https://www.comptia.org/en-us/certifications/dataai

NO.15 Which of the following explains back propagation?

The passage of convolutions backward through a neural network to update weights and biases

The passage of accuracy backward through a neural network to update weights and biases

The passage of nodes backward through a neural network to update weights and biases

The passage of errors backward through a neural network to update weights and biases

NO.16 A data scientist needs to:
Build a predictive model that gives the likelihood that a car will get a flat tire.
Provide a data set of cars that had flat tires and cars that did not.
All the cars in the data set had sensors taking weekly measurements of tire pressure similar to the sensors that will be installed in the cars consumers drive.
Which of the following is the most immediate data concern?

Granularity misalignment

Multivariate outliers

Insufficient domain expertise

Lagged observations

NO.17 During EDA, a data scientist wants to look for patterns, such as linearity, in the data. Which of the following plots should the data scientist use?

Violin

Box-and-whisker

Scatter

Q-Q

NO.18 A data scientist is presenting the recommendations from a monthslong modeling and experiment process to the company’s Chief Executive Officer. Which of the following is the best set of artifacts to include in the presentation?

Methods, data overview, results, recommendations, and charts

Results, recommendations, justifications, and clear charts

Recommendation, charts, justifications, code reviews, and results

Methodology, code snippets, findings, data tables, and p-values

NO.19 SIMULATION
A data scientist needs to determine whether product sales are impacted by other contributing factors. The client has provided the data scientist with sales and other variables in the data set.
The data scientist decides to test potential models that include other information.
INSTRUCTIONS
Part 1
Use the information provided in the table to select the appropriate regression model.
Part 2
Review the summary output and variable table to determine which variable is statistically significant.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

NO.20 A movie production company would like to find the actors appearing in its top movies using data from the tables below. The resulting data must show all movies in Table 1, enriched with actors listed in Table 2.

Which of the following query operations achieves the desired data set?

Perform an INNER JOIN between Table 1 using column Movie, and Table 2 using column Acted_In.

Perform a UNION between Table 1 using column Movie, and Table 2 using column Acted_In.

Perform an INTERSECT between Table 1 using column Movie, and Table 2 using column Acted_In.

Perform a LEFT JOIN on Table 1 using column Movie, with Table 2 using column Acted_In.

NO.21 A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?

Autoregressive

Moving average

Dynamic time warping

Relative strength

NO.22 Which of the following issues should a data scientist be most concerned about when generating a synthetic data set?

The data set consuming too many resources

The data set having insufficient features

The data set having insufficient row observations

The data set not being representative of the population

NO.23 Which of the following is a classic example of a constrained optimization problem?

The cold start problem

The traveling salesman

Calculating local maximum

Calculating gradient descent

NO.24 Which of the following types of machine learning is a GPU most commonly used for?

Deep learning/neural networks

Clustering

Natural language processing

Tree-based

NO.25 A data scientist has constructed a model that meets the minimum performance requirements specified in the proposal for a prediction project. The data scientist thinks the model’s accuracy should be improved, but the proposed deadline is approaching. Which of the following actions should the data scientist take first?

Continue collecting data.

Request additional funding.

Consult the key project stakeholder.

Test additional model specifications.

NO.26 Which of the following problem-solving approaches is a set of guidelines to handle highly variable and not fully apparent situations?

Schedule

Plan

Heuristic

Algorithm

NO.27 An analyst wants to show how the component pieces of a company’s business units contribute to the company’s overall revenue. Which of the following should the analyst use to best demonstrate this breakdown?

Box-and-whisker chart

Sankey diagram

Scatter plot matrix

Residual chart

NO.28 A data scientist is designing a real-time machine-learning model that classifies a user based on initial behavior. The run times of these models are provided in the following table:

Which of the following models should the data scientist recommend for deployment?

XGBoost

Random forest

Decision trees

Artificial neural network

NO.29 Which of the following compute delivery models allows packaging of only critical dependencies while developing a reusable asset?

Thin clients

Containers

Virtual machines

Edge devices

NO.30 Which of the following image data augmentation techniques allows a data scientist to increase the size of a data set?

Clipping

Cropping

Masking

Scaling

NO.31 A data scientist trained a model for departments to share. The departments must access the model using HTTP requests. Which of the following approaches is appropriate?

Utilize distributed computing.

Deploy containers.

Create an endpoint.

Use the File Transfer Protocol.

NO.32 A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?

inner join between Table 1 and Table 2

left join on Table 1 with Table 2

right join on Table 1 with Table 2

outer join between Table 1 and Table 2

NO.33 Given a logistics problem with multiple constraints (fuel, capacity, speed), which of the following is the most likely optimization technique a data scientist would apply?

Constrained

Unconstrained

Non-iterative

Iterative

NO.35 A data scientist is working with a data set that covers a two-year period for a large number of machines. The data set contains:
* Machine system ID numbers
* Sensor measurement values
* Daily timestamps for each machine
The data scientist needs to plot the total measurements from all the machines over the entire time period.
Which of the following is the best way to present this data?

Scatter plot

Line plot

Histogram

Box-and-whisker plot

NO.36 A data analyst is examining the correlation matrix of a new data set to identify issues that could adversely impact model performance. Which of the following is the analyst most likely checking for?

Undersampling

Multicollinearity

Oversampling

Overfitting

NO.37 Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?

Pooling

Input

Output

Hidden

CompTIA DY0-001 Exam Syllabus Topics:

Topic	Details
Topic 1	Specialized Applications of Data Science: This section of the exam measures skills of a Senior Data Analyst and introduces advanced topics like constrained optimization, reinforcement learning, and edge computing. It covers natural language processing fundamentals such as text tokenization, embeddings, sentiment analysis, and LLMs. Candidates also explore computer vision tasks like object detection and segmentation, and are assessed on their understanding of graph theory, anomaly detection, heuristics, and multimodal machine learning, showing how data science extends across multiple domains and applications.
Topic 2	Operations and Processes: This section of the exam measures skills of an AI ML Operations Specialist and evaluates understanding of data ingestion methods, pipeline orchestration, data cleaning, and version control in the data science workflow. Candidates are expected to understand infrastructure needs for various data types and formats, manage clean code practices, and follow documentation standards. The section also explores DevOps and MLOps concepts, including continuous deployment, model performance monitoring, and deployment across environments like cloud, containers, and edge systems.
Topic 3	Mathematics and Statistics: This section of the exam measures skills of a Data Scientist and covers the application of various statistical techniques used in data science, such as hypothesis testing, regression metrics, and probability functions. It also evaluates understanding of statistical distributions, types of data missingness, and probability models. Candidates are expected to understand essential linear algebra and calculus concepts relevant to data manipulation and analysis, as well as compare time-based models like ARIMA and longitudinal studies used for forecasting and causal inference.
Topic 4	Machine Learning: This section of the exam measures skills of a Machine Learning Engineer and covers foundational ML concepts such as overfitting, feature selection, and ensemble models. It includes supervised learning algorithms, tree-based methods, and regression techniques. The domain introduces deep learning frameworks and architectures like CNNs, RNNs, and transformers, along with optimization methods. It also addresses unsupervised learning, dimensionality reduction, and clustering models, helping candidates understand the wide range of ML applications and techniques used in modern analytics.
Topic 5	Modeling, Analysis, and Outcomes: This section of the exam measures skills of a Data Science Consultant and focuses on exploratory data analysis, feature identification, and visualization techniques to interpret object behavior and relationships. It explores data quality issues, data enrichment practices like feature engineering and transformation, and model design processes including iterations and performance assessments. Candidates are also evaluated on their ability to justify model selections through experiment outcomes and communicate insights effectively to diverse business audiences using appropriate visualization tools.