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CompTIA DY0-001 Exam Questions
- 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: 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 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: 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 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.
Free CompTIA DY0-001 Exam Actual Questions
Note: Premium Questions for DY0-001 were last updated On Jul. 27, 2025 (see below)
The term "greedy algorithms" refers to machine-learning algorithms that:
Greedy algorithms build the solution iteratively by choosing at each step the option that appears best at that moment, without reconsidering earlier choices.
Which of the following methods should a data scientist use just before switching to a potential replacement model?
A/B testing lets you compare the current model against the candidate in parallel, measuring performance on live data, before fully switching to the new model.
Which of the following measures would a data scientist most likely use to calculate the similarity of two text strings?
Edit distance quantifies how many single-character insertions, deletions, or substitutions are needed to transform one string into another, making it a direct measure of their similarity.
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 discovers clusters based on density without requiring you to predefine the number of clusters, automatically finding arbitrarily shaped groups and identifying noise points.
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?
The aggregated feedback covers only 80% of respondents, mostly from a few professions and locations, so the model hasn't ''seen'' the remaining 20% (and those underrepresented groups). Its performance on those unseen subsets (out-of-sample data) is therefore the primary concern for how well it will predict the actual vote.
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