DA0-001 Exam Dumps - CompTIA Data+ Certification Exam

The report for the CEO should include YTD total sales, as this will provide a high-level overview of the sales performance of the company and show how it is meeting its annual goals. The other options are not appropriate for the CEO, as they are either too detailed or irrelevant for the report. The sales representatives’ home addresses, line-item SKU numbers, and customers’ first and last names are not related to the sales performance and might compromise the privacy and security of the data. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

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Hierarchical (B) is an older model largely superseded by relational for general transactional systems and is not the primary model referenced for “transactional SQL.”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

Personally Identifiable Information (PII) refers to any data that can be used to identify a specific individual. To comply with privacy policies, it's crucial to mask such information to protect individual privacy.

Option A:Government-issued ID

Rationale:Government-issued IDs (e.g., Social Security numbers, driver's license numbers) are direct identifiers and are considered sensitive PII. Masking this information prevents unauthorized access to critical personal data.

Option B:Address

Rationale:An individual's address is also considered PII, as it can be used to locate and identify a person. Masking addresses helps in protecting personal location information.

Option C:Order ID

Rationale:An order ID is typically a unique identifier for a transaction and does not directly identify an individual.

Option D:Order date

Rationale:The date of an order does not constitute PII, as it doesn't provide information that can identify a person.

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]

 A dashboard is a graphical display that summarizes and presents key performance indicators (KPIs) and metrics for a business or a project. A dashboard should be clear, concise, and easy to understand. To develop a dashboard, one of the best practices is to create a wireframe or a mockup first. A wireframe or a mockup is a low-fidelity sketch or prototype of the dashboard layout and design, which helps to define the scope, requirements, and functionality of the dashboard. Creating a wireframe or a mockup can help to save time and resources, as well as to get feedback from stakeholders and users before deploying the dashboard to production. Therefore, the correct answer is A. References: [Dashboard Design Best Practices: 4 Key Principles | Toptal], [How to Create an Effective Dashboard (with Examples) | Tableau]

For an employer looking to maintain adequate office staffing during winter while tracking storm data, the most effective method would be to use public databases. These databases often contain comprehensive records of weather patterns and storm data collected and verified by reputable meteorological organizations. Utilizing public databases allows for access to historical and real-time data that is crucial for making informed decisions about staffing during adverse weather conditions.

Web scraping (A) is not the most reliable method, as it may involve extracting data from various websites that might not always provide verified or consistent information. Observations © can be subjective and may not cover a wide enough area to be effective for decision-making on a larger scale. Weather surveys (D) could provide insights, but they are not as immediate or comprehensive as the data available in public databases.

In data warehousing, a star schema consists of:

A central fact table (containing measures such as sales, quantities, amounts)

Multiple surrounding dimension tables (such as date, product, customer, location)

A core characteristic of a star schema is that the dimension tables are denormalized:

Each dimension table is usually wide and contains descriptive attributes in a single table (rather than being broken into multiple normalized tables).

This design simplifies queries and improves performance for analytical workloads.

Why the other options are incorrect:

A. It has a tabular structure.All relational schemas (OLTP and OLAP) are tabular; this is not unique to star schemas.

B. It stores transactional data.Transactional data is typically stored in normalized OLTP relational databases, not in star schemas (which are optimized for analytics).

C. It stores unstructured data.Star schema is a structured relational design, not used for unstructured data (e.g., text blobs, images).

Therefore, the defining characteristic listed in the options is: It has denormalized dimension tables (D).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data concepts and environments: schema design (star vs snowflake).

Common data warehousing references describe star schema as: fact table + denormalized dimension tables optimized for analytics.

A data field to data type validator is a tool or a process that checks if the data in each field of a file matches the expected data type, such as text, number, date, etc. A data field to data type validator can help to identify and correct any errors or inconsistencies in the data before importing it into the accounts payable system. This would reduce the number of errors and broken imports, as well as save time and effort for the user.