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| Section | Weight | Objectives |
|---|---|---|
| Data Acquisition and Preparation | 22% | - Data extraction, transformation, and loading (ETL/ELT) - Data cleaning, validation, and quality improvement - Data collection methods and sources - Data integration and normalization |
| Data Concepts and Environments | 20% | - Data types, structures, and formats - Database systems and architectures - Foundational concepts of AI and machine learning in data - Cloud and distributed data environments |
| Data Governance, Quality, and Management | 14% | - Data privacy, security, and compliance - Data governance frameworks and policies - Data lifecycle management and documentation - Data quality standards and monitoring |
| Data Analysis | 24% | - Analytical tools and querying languages - Data modeling and pattern recognition - Statistical methods and techniques - Exploratory and confirmatory data analysis |
| Visualization and Reporting | 20% | - Validation and accuracy of reports - Communicating insights to stakeholders - Dashboard and report development - Design principles for effective visualizations |
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質問 # 75
Which of the following data repositories stores unaltered data?
正解：A
解説：
This question falls under theData Concepts and Environmentsdomain, focusing on data repositories. The task is to identify a repository that stores data in its original, unaltered form.
* Data lake (Option A): A data lake stores raw, unaltered data in its native format (structured, semi- structured, or unstructured), making it the correct choice.
* Data warehouse (Option B): A data warehouse stores processed, structured data, often transformed for analysis, not unaltered.
* Data table (Option C): A data table is a structure within a database, not a repository, and may contain altered data.
* Data factory (Option D): A data factory (e.g., Azure Data Factory) is a data integration service, not a repository for storing data.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed to store unaltered data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
質問 # 76
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?
正解：D
解説：
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.
質問 # 77
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?
正解：C
解説：
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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
質問 # 78
A data analyst is creating a pivot table for a large data set for an upcoming board meeting. Which of the following is the purpose of the pivot table?
正解：B
解説：
A pivot table is used to aggregate, summarize, and analyze large data sets, enabling quick insights such as totals, averages, and groupings.
質問 # 79
Which of the following best enables the retrieval and manipulation of data that is stored in a relational database?
正解：C
解説：
This question pertains to theData Concepts and Environmentsdomain, focusing on tools for interacting with relational databases. The task is to identify the best method for retrieving and manipulating data.
* XML (Option A): XML is a data format, not a language for retrieving or manipulating database data.
* SQL (Option B): SQL (Structured Query Language) is specifically designed for querying and manipulating data in relational databases (e.g., SELECT, UPDATE), making it the best choice.
* Excel (Option C): Excel can analyze data but isn't designed for direct database manipulation.
* JavaScript (Option D): JavaScript is a programming language for web development, not optimized for relational database operations.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases," and SQL is the standard language for relational database operations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
質問 # 80
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