CompTIA DY0-001 CompTIA DataX Exam Exam Practice Test

First, we assume 100 trucks (or 100 predictions), as the percentages are easiest to scale on a base of 100.

Using the confusion matrix:

True Positives (Predicted Fail & Actually Fails): 80 trucks — correct → +1 hr each = +80 hrs

False Positives (Predicted Fail & Actually Succeeds): 20 trucks — incorrect → –4 hrs each = –80 hrs

False Negatives (Predicted Succeed & Actually Fails): 15 trucks — incorrect → –4 hrs each = –60 hrs

True Negatives (Predicted Succeed & Actually Succeeds): 85 trucks — correct → +1 hr each = +85 hrs

Now calculate net hours:

Total gain: 80 hrs (TP) + 85 hrs (TN) = +165 hrs

Total loss: 80 hrs (FP) + 60 hrs (FN) = –140 hrs

Net Impact: 165 – 140 = +25 hours saved

So the correct answer is:

Answer: B (25 hours saved)

However, based on the table provided (which appears to be normalized as percentages), the values apply to a total of 100 predictions. Let's recalculate carefully and validate.

Breakdown:

TP = 80% → 80 × +1 hr = +80 hrs

FP = 20% → 20 × –4 hrs = –80 hrs

FN = 15% → 15 × –4 hrs = –60 hrs

TN = 85% → 85 × +1 hr = +85 hrs

Total hours = +80 + 85 – 80 – 60 = +25 hrs

Final Answer: B. 25 hours saved

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Business cost/benefit analysis based on confusion matrix performance is critical for evaluating model ROI.”

→ LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.

Why the other options are incorrect:

A: |e| is absolute error, not used in standard LASSO objective.

B: e is the error term, but minimization applies to its squared version.

C: Minimizing to exactly 0 is idealistic but not realistic.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO minimizes squared errors with an additional L1 regularization term.”

Elements of Statistical Learning, Chapter 6:“LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint.”

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→ Adjusted R² is specifically designed to evaluate the goodness-of-fit of a regression model while adjusting for the number of predictors. Unlike R², which always increases with more variables, adjusted R² penalizes for adding irrelevant predictors and provides a more accurate measure of model quality.

Why the other options are incorrect:

B: p-values assess significance of individual predictors, not overall model performance.

C: χ² tests are used in categorical data, not regression fit.

D: R² may be misleading when more variables are added — it always increases or stays the same.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 3.2:“Adjusted R² accounts for the number of predictors, making it suitable for comparing models with different numbers of variables.”

Applied Regression Analysis, Chapter 5:“Adjusted R² is used to judge whether adding predictors actually improves the model beyond overfitting.”

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→ Regular expressions (regex) are powerful tools for pattern matching in text. They are ideal for extracting substrings, such as domains, parameters, or specific keywords from URLs or structured text fields.

Why the other options are incorrect:

B: NER is used to extract named entities (like names, places) — not substrings in structured text.

C: LLMs are overkill and not efficient for simple string matching tasks.

D: Find and replace is manual and non-scalable for large data sets.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“Regular expressions provide a flexible method to extract patterns and substrings in structured or semi-structured text.”

Data Cleaning Handbook, Chapter 3:“Regex is the most effective tool for parsing text formats like URLs, emails, or custom tags.”

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→ Line plots are ideal for visualizing data trends over continuous time. In this case, plotting the total daily measurements across a two-year period is a time series task, and a line plot shows progression and pattern over time clearly.

Why the other options are incorrect:

A: Scatter plots are better for relationship exploration, not time trends.

C: Histograms display distribution — not suitable for continuous time trends.

D: Box plots show spread and outliers — not temporal behavior.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.2:“Use line plots for visualizing temporal trends in time-series data.”

Time Series Visualization Guide, Chapter 2:“Line plots are effective for showing cumulative or aggregated values over time.”

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→ The Traveling Salesman Problem (TSP) is a classic example in graph theory. It involves finding the shortest path that visits a set of nodes (cities) and returns to the starting point. Graph theory is used to model nodes (cities) and edges (paths between cities).

Why other options are incorrect:

A: OCR is a computer vision problem — best handled with CNNs or ML image models.

C: Fraud detection can involve graph-based approaches but is typically solved using anomaly detection or classification.

D: One-armed bandit is a reinforcement learning problem — not related to graph theory.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.4:“Graph theory is frequently used in routing and path optimization problems such as the Traveling Salesman.”

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→ Decision trees are supervised classification models that can be used to predict a categorical target variable (e.g., Habitat: Land or Sea) based on input features (e.g., Wrapper color, Wrapper shape, Animal type). They are interpretable, require minimal preprocessing, and are ideal for structured categorical data like this.

Why the other options are incorrect:

A: ARIMA (AutoRegressive Integrated Moving Average) is used for time-series forecasting, not classification.

B: Linear regression is used for predicting continuous numeric values, not categorical variables like "Land" or "Sea".

C: Association rules (like in market basket analysis) are used to discover relationships or co-occurrence among variables, not to build predictive models.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.1 & 4.2:“Decision trees are powerful classifiers for categorical output variables and allow for interpretable models based on feature splits.”

Machine Learning Textbook, Chapter 6:“Decision trees are ideal for early-stage model prototyping when the output is categorical and the data structure is tabular.”

→ Greedy algorithms make decisions based on what appears to be the best (most optimal) choice at that current moment — i.e., a locally optimal decision — without regard to whether this choice will yield the globally optimal solution.

Examples in machine learning:

Decision Tree algorithms (e.g., CART) use greedy approaches by selecting the best split at each node based on information gain or Gini index.

Why the other options are incorrect:

A: This refers to Bayesian updating, not greedy behavior.

B: That describes exhaustive search, not greediness.

C: That aligns more with probabilistic or generative models, not greedy strategies.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.2 (Model Selection Methods):“Greedy algorithms make locally optimal decisions at each step. Decision trees, for instance, use greedy splitting based on current best criteria.”

Elements of Statistical Learning, Chapter 9:“Greedy methods make stepwise decisions that maximize immediate gains — they are fast, but may miss the global optimum.”

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→ In the proof-of-concept phase, the first practical step is model selection — identifying which modeling technique is most appropriate based on the nature of the problem, data, and business goal. Literature reviews are helpful but usually precede model experimentation.

Why the other options are incorrect:

A: Literature review informs planning but isn't the first hands-on step.

B: Performance evaluation comes after models are built.

C: Hyperparameter tuning applies after a model is chosen.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.1:“Model selection is a critical step during early prototyping when evaluating different algorithms for feasibility.”

CRISP-DM Framework – Modeling Phase:“Selecting candidate models is the first step in model development after understanding the data.”

→ A Sankey diagram is ideal for illustrating flow-based relationships, such as how different units or sources contribute to a total. It's especially effective for showing proportions, hierarchy, and decomposition — such as revenue contribution by business units.

Why the other options are incorrect:

A: Box plots show distributions and spread — not contributions or breakdowns.

C: Scatter plot matrix explores relationships between numeric variables, not part-to-whole relationships.

D: Residual charts are diagnostic tools for regression — not for revenue visualization.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.5:“Sankey diagrams are useful for visualizing contributions, flows, and proportional allocations across categories.”

Data Visualization Best Practices, Chapter 7:“Sankey charts are preferred when tracking contributions from multiple inputs to a unified output.”

→ Non-technical business stakeholders value outcome-oriented visuals (charts, dashboards) and the purpose/justification for the modeling work. These artifacts directly communicate impact without overwhelming technical complexity.

Why the other options are incorrect:

C & D: Too technical for a non-technical audience.

E: Useful, but may be too detailed depending on the level of abstraction desired.

F: Data dictionary is better suited for technical handoff — not executive review.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.5:“Business-oriented presentations should emphasize clear visualizations, insights, and executive summaries of model goals.”

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→ In the context of Naive Bayes classifiers, the "naive" assumption refers to the conditional independence of features given the class label. That is, the model assumes each feature contributes independently to the probability of the output class, which simplifies the computation of probabilities.

Why the other options are incorrect:

A: Normal distribution is often assumed for continuous variables, but it's not the naive assumption in Bayes' rule.

C: Uniform distribution refers to equal probability across outcomes, not used here.

D: Homoskedasticity is related to constant variance in regression, not Bayesian classification.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.1:“Naive Bayes assumes all features are conditionally independent given the target class, which allows for efficient computation.”

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→ R² (coefficient of determination) quantifies how much of the variance in the dependent variable is explained by the model. A higher R² means a better fit to the data, making it the metric to maximize for explanatory power in regression analysis.

Why the other options are incorrect:

A: Accuracy is used in classification, not regression.

C: p-values test statistical significance of coefficients, not overall model fit.

D: AUC (Area Under the Curve) applies to classification models, not regression.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“R² is a regression performance metric indicating the proportion of variance explained by the independent variables.”

→ An inner join returns only those records that have matching keys (employee IDs in this case) in both tables. Since each table provides a different attribute for the same entity (employee), an inner join is the most efficient and accurate method when focusing on employees present in both tables.

Why the other options are less ideal:

B & C: Left or right joins would include unmatched data, which may lead to nulls.

D: An outer join brings in all records from both tables and fills nulls where no matches exist, which may introduce irrelevant or incomplete entries.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.2:“Inner joins are most appropriate when combining datasets with matching keys to retain only relevant, intersecting records.”

SQL for Data Analysts, Chapter 3:“Use inner joins when combining tables on a common key to include only matched data for analysis.”

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→ In ridge regression, the model minimizes the sum of squared residuals (errors), with an added penalty term on the magnitude of coefficients (L2 regularization). The residual component specifically is represented by:

→ e² (squared error)

Thus, ridge regression minimizes:

Minimize: ∑(yᵢ − ŷᵢ)² + λ∑(β²)

Why the other options are incorrect:

A: |e| corresponds to L1 loss (used in Lasso).

B: e represents the error term itself, not its minimized quantity.

D: Zero error is ideal but practically unachievable and not the actual loss function being minimized.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.4:“Ridge regression minimizes the squared error term with an L2 penalty.”

Introduction to Statistical Learning, Chapter 6:“Ridge regression uses squared error loss, which emphasizes larger deviations more heavily than linear loss.”

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→ For executive-level presentations, the focus should be on strategic outcomes. Therefore, concise results, clear actionable recommendations, visual summaries (charts), and minimal justifications are best. Technical details such as p-values, code, or full methods are too granular.

Why the other options are incorrect:

A: Too method-heavy for executive audiences.

C: Includes code reviews — not suitable for a CEO.

D: Overly technical for high-level stakeholders.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.5:“Executive communication should focus on outcome-driven recommendations, high-level insights, and actionable visuals.”

Harvard Business Review – Communicating Data to Executives:“Avoid technical detail. Use visuals and clearly stated recommendations supported by business-focused justifications.”

→ FULL OUTER JOIN returns all rows from both tables, inserting NULLs where no match exists. This join includes the maximum possible number of records — all matches, plus all unmatched records from both sides.

Why the other options are incorrect:

A: INNER JOIN returns only matching rows — less total data.

B & C: LEFT/RIGHT JOIN include all rows from one table only.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.2:“A FULL OUTER JOIN maximizes data volume by including all matched and unmatched records from both tables.”

SQL for Data Science, Chapter 4:“Use FULL OUTER JOIN when the goal is to preserve every record from both datasets regardless of match.”

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→ In real-time systems, low latency (short run time) is critical. While the Artificial Neural Network provides the highest accuracy, its 12-minute runtime makes it unsuitable for real-time inference. Random forest is the fastest but offers the lowest accuracy.

XGBoost provides an excellent balance between runtime (5 minutes) and accuracy (90%). It’s well-optimized for performance and scalability, and thus is a strong candidate for real-time classification when balancing both efficiency and predictive quality.

Why the other options are less ideal:

B: Random forest is faster but significantly less accurate.

C: Decision trees have longer run time than XGBoost with only a 2% accuracy improvement.

D: Artificial neural network has the highest accuracy but is too slow for real-time applications.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“In real-time applications, model selection involves a trade-off between accuracy and inference speed. XGBoost offers competitive accuracy with efficient runtime.”

Machine Learning Systems Design Guide, Chapter 7:“XGBoost is well-suited for real-time systems due to its balance of model complexity and fast prediction times.”

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→ Senior executives and the C-suite are primarily interested in decision-support insights rather than technical or academic depth. Thus, appropriate content includes:

C. Final recommendations: Executives need clear actions or decisions.

D. High-level results: Summarized performance, trends, or KPIs without technical jargon.

Why the other options are incorrect:

A: Literature reviews are too detailed and academic.

B: Code is technical and not relevant to business strategy.

E: Statistical tests may overwhelm a non-technical audience.

F: Sharing security keys violates cybersecurity protocols.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.5 (Communication & Visualization):“Executive presentations should include concise, actionable insights and high-level summaries to support strategic decision-making.”

Harvard Business Review – Data Storytelling:“Executives value clear insights, visual summaries, and recommendations. Avoid technical deep dives unless specifically requested.”

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→ A systematic error is a consistent, repeatable error caused by faulty equipment or flawed measurement techniques. Since one sensor consistently over-reports values, this is a classic case of systematic error.

Why the other options are incorrect:

A: Synthetic data is artificially generated — unrelated to sensor malfunction.

C: Heteroskedasticity refers to non-constant variance — not consistent bias.

D: Idiosyncratic errors are random and unpredictable — not consistent.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.4:“Systematic errors arise from consistent biases in measurement devices or methods, requiring calibration or correction.”

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→ Compression is a CPU-intensive process because it requires encoding data into a smaller format, often involving complex algorithms. While memory use is usually moderate, CPU usage can spike significantly, especially during real-time compression or large dataset processing.

Why the other options are incorrect:

A: Library issues are possible but not the most likely issue in compression.

C: Most operating systems support common compression formats (e.g., .zip, .gz).

D: Memory usage is generally lower than CPU usage during compression.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.4:“Compression is compute-intensive and may result in increased CPU utilization, particularly on shared servers or during large batch processes.”

Cloud Data Engineering Guide, Chapter 9:“High CPU usage is a common bottleneck in data compression and decompression processes, especially at scale.”

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→ For small sample sizes (typically n < 30), the Student's t-distribution is preferred over the normal distribution for hypothesis testing because it accounts for the added uncertainty in the estimate of the standard deviation. With 20 observations, the t-distribution is more appropriate and reliable.

Why the other options are incorrect:

A: Power law is used in modeling rare events or heavy-tailed distributions, not hypothesis testing.

B: The normal distribution is more appropriate when the sample size is large.

C: Uniform distribution assumes equal probability — not used in inferential statistics.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.3:“The t-distribution is used for small sample hypothesis testing where the population standard deviation is unknown.”

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→ Optical Character Recognition (OCR) is the process of converting scanned images or hard copy text into machine-encoded text. It is the standard technique for digitizing printed or handwritten content.

Why the other options are incorrect:

A: Word2vec is for generating word embeddings from digital text.

C: Latent Semantic Analysis analyzes semantic structure of existing digital documents.

D: Semantic segmentation is used in image processing for pixel-wise classification — not text extraction.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.3:“OCR converts scanned physical documents into text files that can be searched, analyzed, or stored digitally.”

Practical NLP Applications, Chapter 2:“OCR is a prerequisite for turning printed or written material into structured data suitable for text analytics.”

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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.

→ The model already meets the performance goals outlined in the project proposal. However, since the deadline is near and the data scientist is considering further improvements, the correct approach is to:

→ Consult the key project stakeholder. This ensures transparency and aligns actions with stakeholder priorities — whether to proceed with deployment or invest in further model tuning.

Why the other options are incorrect:

A: Collecting more data requires time and may exceed project scope.

B: Requesting funding is premature and not justified if performance goals are already met.

D: Testing new models takes time and may delay delivery — stakeholder input is needed first.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.1:“Stakeholder engagement is critical in project decision-making, especially when trade-offs exist between quality and timelines.”

CRISP-DM Framework – Evaluation Phase:“Before modifying models that meet objectives, it is essential to consult business stakeholders to align with their expectations.”

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