CompTIA Updated DA0-001 Exam Questions and Answers by zoha

Transactional SQL typically refers to:

OLTP (Online Transaction Processing) workloads

Frequent inserts, updates, and deletes

Strong ACID properties (Atomicity, Consistency, Isolation, Durability)

The standard database model used for this is the relational database:

Data stored in normalized tables with defined relationships (primary/foreign keys)

SQL used as the primary query language

Designed for transactional reliability and integrity

Why the other options are incorrect:

Snowflake schema (A) and Star schema (D) are data warehouse schema designs, used primarily for analytics (OLAP), not for core transactional workloads.

Hierarchical (B) is an older model largely superseded by relational for general transactional systems and is not the primary model referenced for “transactional SQL.”

Therefore, the best answer is Relational (C).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Data concepts: relational databases as the core technology for transactional systems.

Study content: difference between relational OLTP systems vs analytic schemas (star/snowflake).

Python is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language. Python has a simple and expressive syntax that makes it easy to read and write code. Python also has a rich set of libraries and frameworks that support various tasks and applications in data analytics, such as data manipulation, visualization, machine learning, natural language processing, web scraping, and more. Some examples of popular Python libraries for data analytics are pandas, numpy, matplotlib, seaborn, scikit-learn, nltk, and beautifulsoup. Python is different from other data analytics tools that are not programming languages but rather software applications or platforms that provide graphical user interfaces (GUIs) for data analysis and visualization. Some examples of these tools are SAS, Microsoft Power BI, IBM SPSS. Therefore, the correct answer is D. References: [What is Python? | Definition and Examples], [Python Libraries for Data Science]

Answer: B. Ordinary least squares

Ordinary least squares (OLS) is a type of linear regression that is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when: The relationship between the predictor variable(s) and the response variable is reasonably linear.The response variable is a continuous numeric variable1.

In this case, the data analyst is interested in understanding how ice cream consumption (the response variable) is affected by different attributes, such as cost, temperature, and income level (the predictor variables). Assuming that these variables have a linear relationship, OLS can be used to estimate the coefficients of the regression equation that best fits the data.OLS can also provide measures of goodness-of-fit, such as R-squared and adjusted R-squared, and test the significance of the coefficients using t-tests and F-tests2.

Option A is incorrect, as logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable.Use when: The response variable is binary – it can only take on two values1. Ice cream consumption is not a binary variable, but rather a continuous numeric variable.

Option C is incorrect, as Cox regression is used to fit a regression model that describes the relationship between one or more predictor variables and a survival time response variable.Use when: The response variable is the time until an event of interest occurs, such as death, failure, or recovery3. Ice cream consumption is not a survival time variable, but rather a continuous numeric variable.

Option D is incorrect, as polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable.Use when: The relationship between the predictor variable(s) and the response variable is non-linear1. If there is no evidence of non-linearity in the data, polynomial regression may not be appropriate, as it may overfit the data and produce unreliable estimates.