[Jul 20, 2026] 100% Real & Accurate DY0-001 Questions with Free and Fast Updates [Q20-Q42]

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[Jul 20, 2026] 100% Real & Accurate DY0-001 Questions with Free and Fast Updates

Self-Study Guide for Becoming an CompTIA DataAI Certification Exam Expert

NEW QUESTION 20
Which of the following is a key difference between KNN and k-means machine-learning techniques?

KNN operates exclusively on continuous data, while k-means can work with both continuous and categorical data.

KNN performs better with longitudinal data sets, while k-means performs better with survey data sets.

KNN is used for finding centroids, while k-means is used for finding nearest neighbors.

KNN is used for classification, while k-means is used for clustering.

NEW QUESTION 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

NEW QUESTION 22
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?

Word cloud

Edit distance

String indexing

k-nearest neighbors

NEW QUESTION 23
In a modeling project, people evaluate phrases and provide reactions as the target variable for the model.
Which of the following best describes what this model is doing?

Sentiment analysis

Named-entity recognition

TF-IDF vectorization

Part-of-speech tagging

NEW QUESTION 24
Which of the following layer sets includes the minimum three layers required to constitute an artificial neural network?

An input layer, a pooling layer, and an output layer

An input layer, a convolutional layer, and a hidden layer

An input layer, a hidden layer, and an output layer

An input layer, a dropout layer, and a hidden layer

NEW QUESTION 25
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

NEW QUESTION 26
Which of the following modeling tools is appropriate for solving a scheduling problem?

One-armed bandit

Constrained optimization

Decision tree

Gradient descent

NEW QUESTION 27
A data scientist is building a proof of concept for a commercialized machine-learning model. Which of the following is the best starting point?

Literature review

Model performance evaluation

Hyperparameter tuning

Model selection

NEW QUESTION 28
A data scientist is deploying a model that needs to be accessed by multiple departments with minimal development effort by the departments. Which of the following APIs would be best for the data scientist to use?

SOAP

RPC

JSON

REST

NEW QUESTION 29
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

NEW QUESTION 30
A data scientist would like to model a complex phenomenon using a large data set composed of categorical, discrete, and continuous variables. After completing exploratory data analysis, the data scientist is reasonably certain that no linear relationship exists between the predictors and the target. Although the phenomenon is complex, the data scientist still wants to maintain the highest possible degree of interpretability in the final model. Which of the following algorithms best meets this objective?

Artificial neural network

Decision tree

Multiple linear regression

Random forest

NEW QUESTION 31
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

NEW QUESTION 32
Which of the following layer sets includes the minimum three layers required to constitute an artificial neural network?

An input layer, a dropout layer, and a hidden layer

An input layer, a pooling layer, and an output layer

An input layer, a convolutional layer, and a hidden layer

An input layer, a hidden layer, and an output layer

NEW QUESTION 33
Under perfect conditions, E. coli bacteria would cover the entire earth in a matter of days. Which of the following types of models is the best for explaining this type of growth?

Linear

Logarithmic

Polynomial

Exponential

NEW QUESTION 34
A data scientist needs to analyze a company’s chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses.
Which of the following is the most efficient way to identify the chemical businesses’ observations?

Ingest the data from all of the hard drives and perform exploratory data analysis to identify which business is responsible for chemical operations.

Perform analysis on all of the data and create a summary report on the results relevant to chemical operations.

Consult with the business team to identify which sites are responsible for chemical operations and ingest only the relevant data for analysis.

Ingest data from the hard drive containing the most data and present sample results on the chemicaloperations.

NEW QUESTION 35
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.

NEW QUESTION 36
Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?

Pooling

Input

Output

Hidden

NEW QUESTION 37
A data analyst wants to find the latitude and longitude of a mailing address. Which of the following is the best method to use?

One-hot encoding

Binning

Geocoding

Imputing

NEW QUESTION 38
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

NEW QUESTION 39
A data scientist is clustering a data set but does not want to specify the number of clusters present. Which of the following algorithms should the data scientist use?

DBSCAN

k-nearest neighbors

k-means

Logistic regression

NEW QUESTION 40
A data scientist observes findings that indicate that as electrical grids in a country become more and more connected over time, the frequency of brownouts and blackouts in total decrease, and the frequency of major brownouts and blackouts increase. Which of the following distribution metrics could best be identified?

Scale axis magnitudes

Kurtosis

Skewness

Normality

NEW QUESTION 41
A data scientist wants to digitize historical hard copies of documents. Which of the following is the best method for this task?

Word2vec

Optical character recognition

Latent semantic analysis

Semantic segmentation

NEW QUESTION 42
A data scientist is developing a model to predict the outcome of a vote for a national mascot. The choice is between tigers and lions. The full data set represents feedback from individuals representing 17 professions and 12 different locations. The following rank aggregation represents 80% of the data set:

Which of the following is the most likely concern about the model’s ability to predict the outcome of the vote?

Interpolated data

Extrapolated data

In-sample data

Out-of-sample data

CompTIA DY0-001 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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.
Topic 5	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.

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CompTIA DY0-001 Exam Syllabus Topics:

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