DY0-001 Exam Dumps : CompTIA DataX Exam

→ 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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→ The graph represents a classic “elbow curve,” which is often used in clustering (e.g., k-means) to help determine the optimal number of clusters. The point where the curve starts to level off (the “elbow”) reflects the best trade-off between model accuracy and processing efficiency.

In this graph, the elbow visually occurs around k = 10. Beyond that, the processing time continues to decrease, but the marginal gain in clustering quality (or drop in processing time) diminishes.

Why the other options are incorrect:

A: k = 2 underfits the data — too few clusters.

C & D: k = 15 or 20 provides minimal additional benefit in processing but may overcomplicate the model.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 4.2:“The elbow method identifies the optimal number of clusters where the rate of improvement drops significantly.”

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Given the requirements:

Minimized file size

Fast ingestion

Schema preservation (including data types)

The most appropriate format is:

→ Parquet – It is a columnar storage file format developed for efficient data processing. Parquet files are compressed, support schema embedding, and enable fast columnar reads, making them ideal for analytical workloads and big data environments.

Why the other options are incorrect:

A. JSON: Text-heavy and lacks native support for data types/schema.

C. XML: Verbose and has poor performance in storage and ingestion speed.

D. CSV: Flat structure, doesn’t store data types or schema, and can be large in size.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.2 (Data Storage Formats):“Parquet is a preferred format for data analysis as it provides efficient compression and encoding with embedded schema information, making it ideal for minimal storage and fast ingestion.”

Apache Parquet Documentation:“Parquet is designed for efficient data storage and retrieval. It includes schema support and works best for analytics use cases.”

Parquet is a columnar storage format that automatically includes schema (data types), uses efficient compression to minimize file size, and enables very fast reads for analytic workloads.