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| Section | Weight | Objectives |
|---|---|---|
| Visualization | 20% | - Data storytelling and reporting tools - Visualization best practices - Chart and dashboard design principles |
| Data Mining | 25% | - Data collection and extraction methods - Data processing and transformation - Data manipulation and cleaning techniques |
| Data Governance, Quality, and Controls | 15% | - Compliance, privacy, and security controls - Data quality management - Data governance frameworks and policies |
| Data Concepts and Environments | 15% | - Data types, structures, and file formats - Data storage systems and platforms - Data lifecycle and data ecosystems |
| Data Analysis | 25% | - Data interpretation and reporting - Statistical methods and analysis techniques - Trend and pattern identification |
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NEW QUESTION # 27
A data analyst wants to isolate a serial number from a string of code. Which of the following data transformation techniques should the analyst use?
Answer: B
Explanation:
Parsing separates a string into its individual components based on a defined pattern or delimiter.
To isolate a serial number from a larger string of code, the analyst would use parsing to extract the specific portion containing the serial number.
NEW QUESTION # 28
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?
Answer: A
NEW QUESTION # 29
A data analyst receives a request for the current employee head count and runs the following SQL statement:
SELECT COUNT(EMPLOYEE_ID) FROM JOBS
The returned head count is higher than expected because employees can have multiple jobs.
Which of the following should return an accurate employee head count?
Answer: C
Explanation:
Counting distinct employee IDs returns each employee only once, even if the employee appears in multiple job records, which produces an accurate head count.
NEW QUESTION # 30
A company experiences a data breach that exposes some customers' information. A data analyst must categorize the customer data into fully affected, partially affected, and not affected. Which of the following is the best technique to use to quantify the extent of the breach?
Answer: A
Explanation:
Binning groups data into predefined categories or ranges. Classifying customers as fully affected, partially affected, and not affected is a form of binning that enables the analyst to quantify and measure the extent of the breach by placing records into meaningful categories.
NEW QUESTION # 31
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?
Answer: C
Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 32
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