Pass the CompTIA CompTIA Data+ DA0-002 Questions and answers with CertsForce

This question falls under theData Analysisdomain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.

Monday: 455

Tuesday: 346

Wednesday: 382

Thursday: 563

Friday: 887

Saturday: 934

Sunday: 346

The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.

Option A: 346– Correct, as it’s the most frequent value.

Option B: 446– Incorrect, as 446 doesn’t appear in the dataset.

Option C: 455– Incorrect, as 455 appears only once.

Option D: 559– Incorrect, as 559 doesn’t appear in the dataset.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.

This question pertains to theData Governancedomain, focusing on troubleshooting andmaintaining data quality in software processes. The analyst has already tried basic debugging and confirmed compatibility, so the next step involves seeking external resources.

Use the old software and preexisting code, since both were functional (Option A): Reverting to old software avoids solving the issue and may introduce other risks (e.g., security vulnerabilities).

Contact the IT department and inform them that the software has a bug (Option B): Assuming a bug without further investigation is premature, especially since compatibility was confirmed.

Escalate to the department manager and ask for assistance (Option C): Escalation is a later step after exhausting technical troubleshooting options.

Research the issue online and see if a solution is available (Option D): Researching online (e.g., forums, documentation) is a logical next step to find solutions or identify known issues, especially after local debugging fails.

The DA0-002 Data Governance domain includes "data quality control concepts," and researching online is a standard troubleshooting step to maintain data process integrity.

This question falls under theData Acquisition and Preparationdomain, 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.

This question falls under theData Analysisdomain, focusing on techniques for segmenting data. The task is to segment customers based on spending behavior, which involves grouping numerical data (Amount_spent) into categories.

Standardization (Option A): Standardization scales numerical data to a common range (e.g., z-scores), but it doesn’t segment customers into groups.

Concatenate (Option B): Concatenation combines text fields, not numerical data for segmentation.

Binning (Option C): Binning involves grouping numerical data into discrete intervals (e.g., low, medium, high spending), which is ideal for segmenting customers based on spending behavior.

Appending (Option D): Appending combines datasets vertically, not relevant for segmentation.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a common method for segmenting numerical data like spending amounts.

This question falls under theData Concepts and Environmentsdomain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.

This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate visualization to show a relationship between two continuous variables (ages and incomes).

Scatter plot (Option A): A scatter plot displays individual data points on two axes (age vs. income), making it ideal for showing the relationship and potential correlation between two continuous variables.

Area plot (Option B): Area plots are used for showing trends over time, not relationships between two variables.

Bar chart (Option C): Bar charts are better for categorical data comparisons, not continuous variable relationships.

Pie chart (Option D): Pie charts show proportions of a whole, not suitable for showing relationships between variables.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a scatter plot is best for showing the relationship between age and income.

This question falls under theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, which involves understanding different types of data repositories and their characteristics. The task is to identify a repository that can store both unstructured and structured data.

Data store (Option A): A data store is a general term for any data repository, but it’s not specific enough to confirm it stores both unstructured and structured data.

Data silo (Option B): A data silo is an isolated data repository, often structured, and not typically designed for unstructured data.

Data mart (Option C): A data mart is a subset of a data warehouse, focused on structured data for specific business areas, not unstructured data.

Data lake (Option D): A data lake is a centralized repository that stores raw data in its native format, including both structured (e.g., tables) and unstructured (e.g., text, images) data, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is specifically designed to handle both unstructured and structured data.

This question pertains to theData Concepts and Environmentsdomain, focusing on types of server infrastructure. The task is to identify an infrastructure where a company owns and maintainsall servers.

Private (Option A): A private infrastructure (often on-premises) means the company owns and maintains its own servers, typically in a private data center, which matches the requirement.

Cloud (Option B): Cloud infrastructure is managed by third-party providers, not owned by the company.

Hybrid (Option C): Hybrid combines on-premises and cloud, so not all servers are owned by the company.

Public (Option D): Public infrastructure is a cloud model shared across multiple organizations, not owned by the company.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and a private infrastructure requires the company to purchase and maintain its own servers.

This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate visualization for a specific requirement. The task is to show the composition of total sales across three channels, which involves showing proportions.

Pivot table (Option A): A pivot table summarizes data but isn’t a visualization; it’s more for data exploration.

Pie chart (Option B): A pie chart shows the proportion of total sales for each channel (Digital, Store, Online), which is ideal for displaying composition.

KPI card (Option C): A KPI card displays a single metric, not suitable for showing composition across multiple channels.

Box plot (Option D): A box plot shows data distribution (e.g., quartiles), not proportions.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a pie chart is best for showing the composition of totals.

This question falls under theData Analysisdomain, focusing on identifying data quality issues.The table shows counts over time, and the analyst needs to flag an issue in the data.

Completeness (Option A): Completeness refers to missing data, but all rows have values for DateTime and Count.

Outlier (Option B): The count of 245 on 2024-01-02 is significantly higher than other counts (12-13), indicating an outlier that should be investigated for accuracy.

Mismatch (Option C): Mismatch implies inconsistent data types or formats, but the DateTime and Count columns appear consistent except for the last row (addressed separately).

Duplication (Option D): Duplication refers to identical rows, but no rows are identical (same DateTime and Count).

The last row ("00:00:00", 12) has a formatting issue, but the most significant issue for analysis is the outlier (245), as it could skew results. The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as identifying outliers in datasets.