CompTIA DA0-002 Exam Dumps

This question falls under the Data Analysis domain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.

Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.

Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.

Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.

Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods,' and descriptive analytics is best for identifying causes in historical data.

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This question pertains to the Data Concepts and Environments domain, focusing on types of server environments. The servers are located at the company's headquarters, indicating a specific deployment model.

Cloud (Option A): Cloud environments are hosted off-site by third-party providers, not at headquarters.

On-premises (Option B): On-premises environments are located at the company's physical location (e.g., headquarters), which matches the scenario.

Public (Option C): Public environments are cloud-based and shared across multiple organizations, not located at headquarters.

Hybrid (Option D): Hybrid environments combine on-premises and cloud, but the scenario specifies all servers are at headquarters.

The DA0-002 Data Concepts and Environments domain includes understanding 'data environments,' and on-premises describes a server environment located at the company's site.

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This question pertains to the Data Concepts and Environments domain, focusing on file formats for data sharing. The task is to identify the best reason to choose CSV over Excel for sharing data.

CSV files can store different types of encoding (Option A): While CSV files can use different encodings, this isn't the primary reason to choose them over Excel.

CSV files are not vendor-specific (Option B): CSV is a plain-text format that can be opened by any software, unlike Excel files, which are tied to Microsoft Excel, making CSV more interoperable and the best reason for sharing.

CSV files are smaller in size (Option C): CSV files are often smaller due to their simplicity, but this isn't always the primary reason for sharing.

CSV files are easier to change in text editors (Option D): While true, this isn't the most compelling reason for sharing data across systems.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and CSV's vendor-neutral nature makes it ideal for sharing data.

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This question pertains to the Visualization and Reporting domain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.

Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they're not a primary survey design concern for Paralympic athletes.

Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.

Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.

Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.

The DA0-002 Visualization and Reporting domain includes 'translating business requirements to form the appropriate visualization,' and color contrast is a key accessibility factor in survey design for diverse audiences.

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.