CompTIA DY0-001 Exam With Confidence Using Practice Dumps

→ A CROSS JOIN returns the Cartesian product of the two tables — meaning every row from the first table is paired with every row from the second table. If Table A has m rows and Table B has n rows, a CROSS JOIN will return m × n rows, making it the largest possible result set of all JOIN types.

Why the other options are incorrect:

A & B: RIGHT JOIN and LEFT JOIN return matched records plus unmatched rows from one side — but not all possible combinations.

D: INNER JOIN returns only matched rows between tables, typically producing fewer records than a CROSS JOIN.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 5.2:“CROSS JOINs generate the Cartesian product of two datasets and should be used carefully due to the exponential growth in the number of records.”

SQL for Data Scientists, Chapter 3:“CROSS JOINs can produce very large datasets, often unintentionally, due to their non-restrictive matching logic.”

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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.

→ Multicollinearity occurs when independent variables are highly correlated with each other. This can distort coefficient estimates and reduce model interpretability. A correlation matrix is the primary tool used to detect it.

Why the other options are incorrect:

A & C: Under/oversampling relate to class imbalance, not variable correlation.

D: Overfitting is related to model complexity, not directly observable via a correlation matrix.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.2:“Correlation matrices are used to detect multicollinearity — high correlations among predictors that may destabilize models.”