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CompTIA DataX Certification Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which of the following image data augmentation techniques allows a data scientist to increase the size of a data set?

A. Cropping
B. Scaling
C. Masking
D. Clipping

Answer: A

Explanation:
# 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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NEW QUESTION # 48
Given a logistics problem with multiple constraints (fuel, capacity, speed), which of the following is the most likely optimization technique a data scientist would apply?

A. Non-iterative
B. Iterative
C. Unconstrained
D. Constrained

Answer: D

Explanation:
# This is a classic constrained optimization problem: the boats have fuel, volume, and speed constraints. The goal is to maximize box transport within the fixed limits (e.g., fuel). Constrained optimization methods are explicitly designed to handle such problems.
Why other options are incorrect:
* B: Unconstrained methods do not account for fuel or capacity limits - inappropriate.
* C: Most real-world constrained problems require iterative approaches for convergence.
* D: Iterative may be part of solving, but it's not a type of optimization - constrained is the category.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.4:"Constrained optimization is used when variables must meet certain limitations or bounds."
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NEW QUESTION # 49
A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?

A. left join on Table 1 with Table 2
B. inner join between Table 1 and Table 2
C. right join on Table 1 with Table 2
D. outer join between Table 1 and Table 2

Answer: B

Explanation:
# 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."
-

NEW QUESTION # 50
A data scientist trained a model for departments to share. The departments must access the model using HTTP requests. Which of the following approaches is appropriate?

A. Create an endpoint.
B. Deploy containers.
C. Use the File Transfer Protocol.
D. Utilize distributed computing.

Answer: A

Explanation:
# Creating an endpoint allows other systems or departments to access the trained model via HTTP requests.
This typically involves exposing the model as a RESTful API, allowing it to be queried by web-based systems.
Why the other options are incorrect:
* A: Distributed computing refers to computation, not access over HTTP.
* B: Containers are useful for deployment, but the endpoint enables access.
* D: FTP is used for file transfer, not model inference via HTTP.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.4:"Endpoints are used to expose models to external consumers over HTTP protocols, often using REST APIs."
* ML Deployment Best Practices, Chapter 3:"RESTful endpoints provide real-time access to model predictions and are key for multi-team collaboration."

NEW QUESTION # 51
Which of the following JOINS would generate the largest amount of data?

A. RIGHT JOIN
B. LEFT JOIN
C. INNER JOIN
D. CROSS JOIN

Answer: D

Explanation:
# 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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NEW QUESTION # 52
......

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