Free Practice Questions for CompTIA DY0-001 Exam

→ A Normal distribution is appropriate for modeling variables that cluster around a central mean and have natural variability — such as bus arrival times around a scheduled time. Even though the bus is scheduled hourly, real-world factors (traffic, weather, etc.) will cause actual arrival times to vary normally around the scheduled mean.

Why the other options are incorrect:

A: Binomial is for discrete yes/no trials, not continuous time modeling.

B: Exponential models time between events, typically memoryless — not suitable for arrival distributions with a known mean and variance.

D: Poisson models event counts per time interval, not the timing of continuous events like arrival times.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.3:“Normal distributions are appropriate for modeling real-world continuous variables that fluctuate around a central tendency, such as scheduled processes.”

Statistics for Data Science, Chapter 4 – Distributions:“Arrival times of periodic services often approximate a normal distribution when influenced by continuous variation.”

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→ Granularity misalignment refers to a mismatch between the level of detail in the predictor variables and the event being predicted.

In this case, flat tires are likely discrete, infrequent events, while tire pressure is measured weekly. If the prediction model is trying to link a specific tire pressure value to a binary outcome (flat tire: yes/no), and the timing doesn't align precisely, the predictor variable (pressure) may not be granular enough to accurately associate with the event.

Why the other options are incorrect:

B: While outliers can exist, they are not the most immediate concern given the time-series nature of the data.

C: While domain expertise is helpful, it doesn't directly address the data structure issue.

D: Lagged observations can be engineered in modeling but aren’t the primary problem here.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 3.1 (Data Granularity):“Granularity misalignment occurs when the temporal or spatial resolution of features does not align with the prediction target.”

Data Science Process Guide, Section 2.3:“Predictive performance can suffer when temporal mismatch exists between observations and outcomes. Granularity issues must be resolved prior to modeling.”

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→ LASSO (Least Absolute Shrinkage and Selection Operator) regression performs both variable selection and regularization by adding an L1 penalty to the loss function. It shrinks less important feature coefficients to zero, effectively performing feature selection — perfect for identifying the most influential predictors.

Why the other options are incorrect:

A: OLS uses all predictors and doesn’t perform feature selection.

B: Ridge regression applies an L2 penalty, shrinking coefficients but keeping all predictors.

C: Weighted least squares adjusts for heteroscedasticity but doesn’t reduce variable count.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO performs feature selection by zeroing out coefficients of less significant predictors.”

Statistical Learning Textbook, Chapter 6:“LASSO regression is ideal when model interpretability and variable reduction are important.”

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→ Sentiment analysis refers to using machine learning or NLP techniques to determine the sentiment or emotional tone behind a body of text (e.g., positive, neutral, or negative). When people provide reactions to phrases, the model is learning to associate language with subjective emotion or opinion.

Why the other options are incorrect:

B: NER identifies entities (e.g., locations, organizations) — not emotions.

C: TF-IDF is a feature engineering method, not a modeling goal.

D: POS tagging classifies words by their grammatical function — not sentiment.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“Sentiment analysis models associate textual input with subjective labels, such as emotional response or polarity.”

Applied Text Analytics, Chapter 8:“When modeling user reactions to text, sentiment classification techniques are commonly employed.”

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→ The provided equation represents an autoregressive model of order 1 (AR(1)). It describes Xt as a function of its immediately prior value (Xt−1) plus white noise.

Key identifiers:

No differencing (so not ARIMA).

No moving average term (so not ARMA).

No seasonal component (so not SARIMA).

Why the other options are incorrect:

A: ARIMA(1,1,1) includes integration and MA terms, which are absent here.

B: ARMA(1,1) includes both AR and MA terms, but only AR is present.

C: SARIMA involves seasonal and differencing components — not applicable here.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.5:“AR(p) models describe a variable as dependent on its previous values with no differencing or moving average.”

Time Series Analysis Textbook, Chapter 4:“Xt = ϕXt-1 + εt describes an AR(1) process when εt is white noise.”

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→ Edit distance (also known as Levenshtein distance) measures how many single-character edits (insertions, deletions, or substitutions) are needed to transform one string into another. It’s a common metric for assessing string similarity, especially in natural language processing (NLP) tasks.

Why the other options are incorrect:

A: Word clouds visualize word frequency, not similarity.

C: String indexing is a method for referencing string positions, not comparison.

D: k-NN is a classification algorithm, not a string similarity measure.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 6.3:“Edit distance is a key similarity metric in text comparison tasks, particularly in cleaning or matching string records.”

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→ Cropping involves selecting portions of an image to create multiple training samples from one image. This technique helps increase dataset size and variability, which improves model generalization.

Why the other options are incorrect:

A: Clipping typically refers to limiting pixel values, not augmentation.

C: Masking hides or removes parts of an image — used more in object detection or inpainting, not to expand the dataset.

D: Scaling changes the image size but doesn’t create new samples.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 6.3:“Cropping is a data augmentation strategy that allows for synthetic expansion of the dataset by generating multiple views.”

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→ REST (Representational State Transfer) is a web-based API style that is widely adopted for its simplicity, scalability, and use of standard HTTP methods (GET, POST, PUT, DELETE). It is stateless and can be consumed easily by multiple systems and departments with minimal integration work.

Why the other options are incorrect:

A: SOAP is heavy, XML-based, and requires more development overhead.

B: RPC is lower-level and not well-suited for scalable, modern web services.

C: JSON is a data format, not an API protocol.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.4 (API and Model Deployment):“REST APIs are preferred for exposing models to various consumers due to their simplicity, platform-agnostic nature, and use of standard HTTP.”

Data Engineering Design Patterns, Section 6:“RESTful services enable easy integration of machine learning models with front-end and enterprise systems.”

RESTful APIs use standard HTTP methods and lightweight data formats (typically JSON), making them easy for diverse teams to integrate with minimal effort and without heavy tooling.

→ CI/CD (Continuous Integration / Continuous Deployment) is a DevOps methodology that automates the building, testing, and deployment of code. It allows teams to iteratively release updates and improvements in a reliable and scalable manner.

Why the other options are incorrect:

A: Virtualization provides environment emulation but doesn’t manage code releases.

B: Markdown is a documentation tool — unrelated to deployment automation.

C: Code isolation refers to modular programming, not automation pipelines.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.3:“CI/CD pipelines streamline model deployment through automation, allowing continuous integration and delivery of updates.”

DevOps for Data Science, Chapter 4:“CI/CD supports fast and reliable code iterations by automatically testing and deploying to production environments.”

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→ Since the model must update daily based on new data, retraining must be fast enough to meet daily deadlines. Therefore, training time is the critical constraint — it determines whether pricing updates can be executed promptly.

Why the other options are incorrect:

A: Deployment time is a one-time or infrequent process.

C: Development time is less critical once the model is built.

D: Data is already collected daily — assumed to be available.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.4:“Time-sensitive applications such as daily pricing require fast model retraining, making training time a critical factor.”

Real-Time ML Deployment Handbook, Chapter 6:“Retraining time is the bottleneck in time-constrained systems that adapt to fresh inputs regularly.”

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