CompTIA DA0-002 Dumps in PDF and Online - Page: 4

QUESTION: 17

Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

To gather information and resources about the data
To find the content and specific attributes for a dataset
To find a summary of basic information about the dataset
To gather information about the availability of the data

Answer(s): B

Explanation:

This question is part of the Data Concepts and Environments domain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.

To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary's purpose.

To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.

To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.

To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

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QUESTION: 18

A data analyst is joining two tables with different content and one common field.
Which of the following should the analyst do to most efficiently meet this requirement?

Match the records of the related columns and merge the tables.
Create a cluster to facilitate data integration between the tables.
Explode both tables to identify unique values and reorder the fields in one table.
Append the values of the matching columns and concatenate the other data fields.

Answer(s): A

Explanation:

This question falls under the Data Acquisition and Preparation domain, focusing on combining data from multiple tables. The tables have different content but share a common field, indicating a join operation.

Match the records of the related columns and merge the tables (Option A): This describes a join operation, where records are matched on the common field (e.g., a key like Customer_ID) and the tables are merged, which is the most efficient method.

Create a cluster to facilitate data integration between the tables (Option B): Clustering is a machine learning technique, not a method for joining tables.

Explode both tables to identify unique values and reorder the fields in one table (Option C):
Exploding is used in nested data (e.g., JSON arrays), and this approach is overly complex and unnecessary.

Append the values of the matching columns and concatenate the other data fields (Option D):
Appending stacks tables vertically, and concatenation applies to text, neither of which is appropriate for joining tables with a common field.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables using a common field.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

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QUESTION: 19

A data analyst pulls a table similar to the following one:

ID Type TypeID Phone

1 Full Time Full Time 1 Mobile

2 Part Time Part Time 2 Work

3 Full Time Full Time 3 Mobile

Which of the following best explains the data issue with TypeID?

Redundancy
Outlier
Missing data
Duplication

Answer(s): A

Explanation:

This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn't applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.

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QUESTION: 20

Which of the following AI types is the best option for time-series forecasting?

Generative AI
Foundational models
Natural language processing
Robotic process automation

Answer(s): B

Explanation:

Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities.
While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.

In reality, the best AI types specifically designed for time-series forecasting include:

Recurrent Neural Networks (RNNs), especially LSTMs and GRUs: These architectures are designed to handle sequential data and capture temporal dependencies.

Transformer Networks: Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.

Traditional statistical models: ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.

Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.

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QUESTION: 21

A data analyst is following up on a recent, company-wide data audit of customer invoice data. Which of the following is the best option for the analyst to use?

PCI DSS
GDPR
ISO
PII

Answer(s): B

Explanation:

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.

PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn't specified.

GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It's the most relevant for a company-wide data audit.

ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.

PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.
The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

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QUESTION: 22

A database administrator needs to implement security triggers for an organization's user information database.
Which of the following data classifications is the administrator most likely using? (Select two).

Public
Open
Sensitive
Non-Sensitive
Private
Encrypted

Answer(s): C,E

Explanation:

This question pertains to the Data Governance domain, focusing on data classification for security purposes. User information databases typically contain personal data, and security triggers (e.g., alerts for unauthorized access) require classifying data to determine protection levels.

Public (Option A): Public data is openly accessible (e.g., company brochures), not suitable for user information requiring security triggers.

Open (Option B): Open isn't a standard data classification; it's similar to public and not applicable here.

Sensitive (Option C): Sensitive data includes information that, if exposed, could cause harm (e.g., user emails, roles), which fits user information and warrants security triggers.

Non-Sensitive (Option D): Non-sensitive data doesn't require protection, so it wouldn't need security triggers.

Private (Option E): Private data includes PII (e.g., names, addresses) in user information databases, requiring security measures like triggers to protect against breaches.

Encrypted (Option F): Encrypted refers to a data state, not a classification; data can be classified as private or sensitive and then encrypted.

The DA0-002 Data Governance domain includes "data quality control concepts," such as classifying data to apply appropriate security measures. Sensitive and private classifications are most relevant for user information.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

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QUESTION: 23

A data analyst is analyzing the following dataset:

Transaction Date Quantity Item Item Price

12/12/12 11 USB Cords 9.99

11/11/11 3 Charging Block 8.89

10/10/10 5 Headphones 50.15

Which of the following methods should the analyst use to determine the total cost for each transaction?

Parsing
Scaling
Compressing
Deriving

Answer(s): D

Explanation:

This question falls under the Data Analysis domain, focusing on calculating new values from existing data. The task is to determine the total cost per transaction, which involves multiplying Quantity by Item Price.

Parsing (Option A): Parsing involves breaking down data (e.g., splitting a string), not calculating totals.

Scaling (Option B): Scaling adjusts numerical values to a common range (e.g., normalization), not relevant for calculating totals.

Compressing (Option C): Compressing reduces data size, not applicable to calculating costs.

Deriving (Option D): Deriving involves creating new data fields by performing calculations on existing ones (e.g., Total Cost = Quantity × Item Price), which fits the task.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as deriving new fields through calculations to analyze data.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

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QUESTION: 24

A senior manager needs a report that can be generated and accessed at any time.
Which of the following delivery methods should a data analyst use?

Ad hoc
Dynamic
Self-service
Static

Answer(s): C

Explanation:

This question is part of the Visualization and Reporting domain, focusing on report delivery methods. The requirement for a report that can be generated and accessed at any time suggests user-driven access.

Ad hoc (Option A): Ad hoc reports are one-time, on-demand reports, not designed for anytime access by the user.

Dynamic (Option B): Dynamic reports allow interactivity but don't necessarily imply user-generated access at any time.

Self-service (Option C): Self-service reporting allows users (e.g., the senior manager) to generate and access reports on-demand at any time, fitting the requirement.

Static (Option D): Static reports are fixed and don't allow on-demand generation by the user.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and self-service is ideal for anytime access.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.

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CompTIA Data+ (2025) DA0-002 Dumps in PDF

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