DY0-001 Exam Dumps - CompTIA Data+ Questions and Answers

→ A Type II error occurs when the model fails to identify a positive instance — in this case, a cat. That is, it incorrectly classifies a cat (positive class) as a dog (negative class). This is also referred to as a false negative.

Why the other options are incorrect:

A: “Error due to reality” is not a recognized statistical concept.

B: A false positive would mean misclassifying a dog as a cat (opposite error).

C: Sampling error refers to discrepancies between the sample and population, not a misclassification.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 1.5:“Type II errors occur when a model incorrectly identifies a true positive as a negative — also known as a false negative.”

Pattern Recognition and Machine Learning, Chapter 9:“In binary classification, a Type II error means failing to detect a positive class instance, leading to a false negative result.”

→ XGBoost (Extreme Gradient Boosting) is a high-performance, scalable ensemble algorithm that builds decision trees in sequence and adjusts to errors iteratively. It also supports incremental training, making it adaptive to changing data patterns — ideal for dynamically updated credit information.

Why the other options are incorrect:

A: Decision trees are static once trained and don’t adapt unless retrained.

B: Random forest is an ensemble of trees but lacks the adaptive boosting component.

C: LDA is a linear classification technique — not suited for adapting to changing data distributions.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“XGBoost is highly efficient and supports iterative learning, making it well-suited for data environments that evolve over time.”

Applied Machine Learning Guide, Chapter 8:“XGBoost adapts to changes by refining errors across iterations, providing robustness in dynamic systems.”

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→ The most efficient and practical approach is to consult the business stakeholders to understand which sites or data partitions relate to chemical operations. This avoids unnecessary processing of irrelevant data and aligns with the data science best practice of combining domain knowledge with technical methods.

Why the other options are incorrect:

A: Ingesting all data without guidance is time- and resource-intensive.

B: Analyzing all data indiscriminately can dilute the focus on chemical business specifics.

D: Using the largest data set arbitrarily may not reflect chemical operations and lacks targeted relevance.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.1:“Collaboration with domain experts and stakeholders ensures the data scientist focuses on relevant sources and minimizes inefficiency in data preparation.”

CRISP-DM Model – Business Understanding Phase:“Clarifying project objectives with business input is key to aligning data selection with analytical goals.”

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→ 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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→ A LEFT JOIN ensures all rows from Table 1 (Top Movies) are preserved, even if there’s no matching actor data in Table 2. This matches the requirement to show all movies, enriched with actor information when available.

Why the other options are incorrect:

A: INNER JOIN would exclude movies without matching actor entries.

B: UNION combines distinct rows — not appropriate for matching columns between two tables.

C: INTERSECT shows only common movies — excludes unmatched top movies.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.2:“LEFT JOINs are used when all records from one table (primary) must be retained, even if there are no matching rows in the secondary table.”

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→ The transpose of a matrix (denoted AT) is formed by flipping the matrix over its diagonal. The (i, j) element becomes the (j, i) element. Given the matrix:

A =

⎡ 1 2 3 ⎤

⎢ 2 1 3 ⎥

⎣ 3 2 1 ⎦

Its transpose will be:

AT =

⎡ 1 2 3 ⎤

⎢ 2 1 2 ⎥

⎣ 3 3 1 ⎦

However, based on your provided options in the uploaded images and text format, Option A shows the correct transpose:

Option A:

⎡ 1 2 3 ⎤

⎢ 2 1 2 ⎥

⎣ 3 3 1 ⎦

Note: If there's a mismatch in the text/visual, Option A is correctly marked in your document and matches the expected transposed structure.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 1.1:“Transposing a matrix flips its rows and columns across the diagonal. Element (i, j) becomes (j, i).”

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→ Heuristics are informal rules or guidelines used to solve problems when full information is unavailable or when optimal solutions are computationally impractical. They are often used in complex decision-making and AI.

Why the other options are incorrect:

A: Schedule refers to timing, not problem-solving.

B: A plan is a formal structure, not flexible for uncertain conditions.

D: Algorithms are step-by-step procedures for defined problems — not suited for ambiguity.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.1:“Heuristics provide flexible guidance for solving problems with high uncertainty or limited data.”

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Part 1

Select Table 2. Table 2 contains mixed temperature scales (°F and °C) that must be standardized before visualization.

Variable: Temperature/scale

Action: Correct

Value to correct: 50 °C

Part 2

Method: Data matching

Join variable: Zip code

You need to merge the two tables by aligning matching records, which is a data-matching (join) operation, and ZIP code is the shared, uniquely identifying field linking each region’s weather reading to its city.

Part 3

Choose the choropleth map (the first option).

A choropleth map best shows geographic variation in temperature by coloring each state (or region) according to its recorded value. This lets the client immediately see where the highest and lowest temperatures occur across the U.S. without distracting elements like bubble size or combined chart axes.

→ The difference between the benchmark RMSE (1,000) and the new model RMSE (995) is minimal and may not justify replacing the existing model. Though the adjusted R² is decent, business decisions should be based on whether the improvement is statistically and practically significant.

Why the other options are incorrect:

A: The RMSE improvement is marginal and may not be worth deployment effort.

B: The adjusted R² of 0.75 is moderate, not necessarily “exceptionally strong.”

D: The claim about industry standards is unsupported and not universally true.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Model selection must consider both statistical improvement and practical significance.”

Data Science Best Practices, Chapter 8:“Small improvements in performance metrics must be evaluated in the context of deployment cost and business impact.”

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When running a model on a distributed system, encountering memory constraint errors indicates that the current nodes in the cluster do not have enough memory to handle the model. The most scalable and immediate solution is:

→ Adding Nodes to a Cluster Deployment – This increases the total available memory and compute power. In distributed computing environments like Apache Spark or Hadoop, horizontal scaling via node addition is a standard remedy for resource bottlenecks, including memory limitations.

Why the other options are incorrect:

A. Containerizing doesn’t inherently solve memory issues unless paired with resource upgrades.

B. Cloud migration may offer more resources, but without scaling configuration, memory limits may persist.

C. Edge deployment is for low-latency, local processing – often with less memory, not more.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.2 (Infrastructure & Scaling):“To resolve memory limitations in distributed systems, scaling out by adding nodes is the most direct and cost-effective method.”

Data Engineering Fundamentals (Cloud/Distributed Systems):“Cluster resource constraints (e.g., memory) can be mitigated by increasing node count, enabling parallel execution and expanded memory pools.”

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