{"id":15980,"date":"2026-09-18T10:40:26","date_gmt":"2026-09-18T10:40:26","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=15980"},"modified":"2026-09-18T10:40:26","modified_gmt":"2026-09-18T10:40:26","slug":"comptia-data-da0-002-practice-test-questions-and-exam-dumps-part17-q321-340","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/comptia-data-da0-002-practice-test-questions-and-exam-dumps-part17-q321-340\/","title":{"rendered":"CompTIA Data+ DA0-002 Practice Test Questions and Exam Dumps Part17 Q321-340"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>CompTIA Data+ DA0-002 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

Question 321<\/b><\/h3>\n

Which statistical measure identifies the middle value when a dataset is arranged in ascending or descending order?<\/b><\/p>\n

    \n
  1. Mode<\/span><\/li>\n
  2. Mean<\/span><\/li>\n
  3. Median<\/span><\/li>\n
  4. Variance<\/span><\/li>\n<\/ol>\n

    Correct Answer: 3<\/b><\/p>\n

    Explanation<\/b><\/h3>\n

    The median is the middle value of an ordered dataset. If there is an odd number of observations, the median is the single middle value. If there is an even number, it is generally calculated as the average of the two middle values. The median is less sensitive to extreme values than the mean, making it useful for skewed datasets. Mode identifies the most frequently occurring value, mean calculates the arithmetic average, and variance measures dispersion. Analysts often compare the mean and median to understand whether a distribution may be influenced by unusually high or low observations.<\/span><\/p>\n

    Question 322<\/b><\/h3>\n

    Which SQL clause is commonly used to sort query results alphabetically or numerically?<\/b><\/p>\n

      \n
    1. ORDER BY<\/span><\/li>\n
    2. GROUP BY<\/span><\/li>\n
    3. WHERE<\/span><\/li>\n
    4. HAVING<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1<\/b><\/p>\n

      Explanation<\/b><\/h3>\n

      ORDER BY sorts the rows returned by a SQL query according to one or more columns or expressions. It can sort values in ascending order using ASC or descending order using DESC. For example, <\/span>ORDER BY revenue DESC<\/span> places higher revenue values before lower ones. GROUP BY creates groups for aggregation, WHERE filters rows, and HAVING filters grouped results. Analysts frequently use ORDER BY to identify top-performing records, arrange dates chronologically, or present results in a meaningful sequence for reporting and analysis.<\/span><\/p>\n

      Question 323<\/b><\/h3>\n

      Which data integration process extracts information from multiple source systems and combines it into a unified target dataset?<\/b><\/p>\n

        \n
      1. Data encryption<\/span><\/li>\n
      2. Data integration<\/span><\/li>\n
      3. Data masking<\/span><\/li>\n
      4. Data deletion<\/span><\/li>\n<\/ol>\n

        Correct Answer: 2<\/b><\/p>\n

        Explanation<\/b><\/h3>\n

        Data integration combines information from multiple sources into a unified view or target system. Sources may include databases, APIs, spreadsheets, applications, files, and cloud services. Integration can involve extraction, transformation, matching, validation, and loading. The goal is to make related information available consistently for reporting, analysis, or operational use. Encryption protects confidentiality, masking hides sensitive values, and deletion removes information. Effective integration requires attention to differences in schemas, formats, identifiers, data definitions, update frequencies, and quality between source systems.<\/span><\/p>\n

        Question 324<\/b><\/h3>\n

        Which statistic is most appropriate for describing the spread of observations around the mean in the original units of measurement?<\/b><\/p>\n

          \n
        1. Variance<\/span><\/li>\n
        2. Standard deviation<\/span><\/li>\n
        3. Percentile<\/span><\/li>\n
        4. Mode<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2<\/b><\/p>\n

          Explanation<\/b><\/h3>\n

          Standard deviation measures the spread of observations around the mean and is expressed in the same units as the original variable. This makes it easier to interpret than variance, which is expressed in squared units. A smaller standard deviation indicates that observations tend to be closer to the mean, while a larger value indicates greater dispersion. Percentiles describe relative position, and mode identifies the most frequent value. Analysts commonly use standard deviation with the mean to summarize distributions, particularly when the data is reasonably symmetric or approximately normally distributed.<\/span><\/p>\n

          Question 325<\/b><\/h3>\n

          Which SQL statement removes selected rows from an existing table while leaving the table structure intact?<\/b><\/p>\n

            \n
          1. DROP TABLE<\/span><\/li>\n
          2. DELETE<\/span><\/li>\n
          3. REMOVE TABLE<\/span><\/li>\n
          4. TRUNCATE DATABASE<\/span><\/li>\n<\/ol>\n

            Correct Answer: 2<\/b><\/p>\n

            Explanation<\/b><\/h3>\n

            DELETE removes rows from an existing table while preserving the table itself and its structure. A WHERE clause can be used to identify specific rows, such as <\/span>DELETE FROM orders WHERE status = ‘Cancelled’<\/span>. Without an appropriate WHERE clause, DELETE may remove all rows from the table. DROP TABLE removes the table object itself, including its structure, while the other options are not standard SQL commands for this purpose. Analysts and database administrators should carefully validate DELETE statements before execution because accidental removal of records can affect downstream reporting and analysis.<\/span><\/p>\n

            Question 326<\/b><\/h3>\n

            Which data quality issue occurs when the same customer appears multiple times as separate records?<\/b><\/p>\n

              \n
            1. Duplicate data<\/span><\/li>\n
            2. Invalid data<\/span><\/li>\n
            3. Stale data<\/span><\/li>\n
            4. Incomplete data<\/span><\/li>\n<\/ol>\n

              Correct Answer: 1<\/b><\/p>\n

              Explanation<\/b><\/h3>\n

              Duplicate data occurs when the same real-world entity or event is represented by multiple records when only one record should exist. For example, a customer might appear twice because of repeated imports or differences in name formatting. Duplicates can inflate counts, revenue totals, customer numbers, and other analytical measures. Analysts can use matching rules, unique identifiers, and deduplication techniques to identify and resolve such records. Invalid data violates defined rules, stale data is outdated, and incomplete data contains missing information. Duplicate detection is therefore an important data-quality step before aggregation.<\/span><\/p>\n

              Question 327<\/b><\/h3>\n

              Which type of chart is most suitable for comparing values across a small number of discrete categories?<\/b><\/p>\n

                \n
              1. Bar chart<\/span><\/li>\n
              2. Line chart<\/span><\/li>\n
              3. Scatter plot<\/span><\/li>\n
              4. Histogram<\/span><\/li>\n<\/ol>\n

                Correct Answer: 1<\/b><\/p>\n

                Explanation<\/b><\/h3>\n

                A bar chart is commonly used to compare numerical values across discrete categories. For example, a business can use a bar chart to compare sales across five product categories or customers across several regions. Bar length or height provides an easy visual comparison between categories. Line charts are generally better for trends over an ordered axis such as time, scatter plots examine relationships between numerical variables, and histograms show numerical distributions. Clear category labels and an appropriate baseline help prevent visual misinterpretation.<\/span><\/p>\n

                Question 328<\/b><\/h3>\n

                Which SQL function returns the smallest value in a column?<\/b><\/p>\n

                  \n
                1. LOW()<\/span><\/li>\n
                2. MIN()<\/span><\/li>\n
                3. SMALL()<\/span><\/li>\n
                4. BOTTOM()<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 2<\/b><\/p>\n

                  Explanation<\/b><\/h3>\n

                  MIN() returns the smallest value from a specified column or expression. For example, <\/span>SELECT MIN(price) FROM products<\/span> can identify the lowest product price. MIN() can also be combined with GROUP BY to determine minimum values for individual categories, such as the lowest order value for each region. MAX() returns the largest value, AVG() calculates the average, and COUNT() counts records. Analysts should consider filtering conditions because a WHERE clause can determine which observations are included in the MIN() calculation.<\/span><\/p>\n

                  Question 329<\/b><\/h3>\n

                  Which term describes information that provides context about a dataset, such as its source, owner, creation date, and field definitions?<\/b><\/p>\n

                    \n
                  1. Metadata<\/span><\/li>\n
                  2. Encryption<\/span><\/li>\n
                  3. Transaction data<\/span><\/li>\n
                  4. Primary data<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1<\/b><\/p>\n

                    Explanation<\/b><\/h3>\n

                    Metadata is information about data that provides context and helps users understand how a dataset should be interpreted and managed. Metadata may include source system, owner, creation date, update frequency, field definitions, data types, lineage, and sensitivity classification. Good metadata improves data discovery, governance, quality management, and analytical consistency. Encryption protects information from unauthorized access, while transaction data records business events. Primary data generally refers to information collected directly for a particular purpose. Analysts rely on metadata to understand datasets before using them for analysis.<\/span><\/p>\n

                    Question 330<\/b><\/h3>\n

                    Which statistical concept represents the probability that an observed result or a more extreme result could occur under a specified statistical hypothesis?<\/b><\/p>\n

                      \n
                    1. Mean<\/span><\/li>\n
                    2. P-value<\/span><\/li>\n
                    3. Median<\/span><\/li>\n
                    4. Standard deviation<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2<\/b><\/p>\n

                      Explanation<\/b><\/h3>\n

                      A p-value is a statistical measure used in hypothesis testing to indicate how compatible observed data is with a specified null hypothesis. A smaller p-value can provide evidence against the null hypothesis under the assumptions of the statistical test. However, a p-value is not the probability that the null hypothesis itself is true. Its interpretation depends on the test, assumptions, study design, and significance threshold. Analysts should consider effect size, confidence intervals, practical significance, and data quality rather than relying on a p-value alone when evaluating findings.<\/span><\/p>\n

                      Question 331<\/b><\/h3>\n

                      Which type of data is typically organized into tables with rows, columns, and predefined data types?<\/b><\/p>\n

                        \n
                      1. Unstructured data<\/span><\/li>\n
                      2. Structured data<\/span><\/li>\n
                      3. Multimedia data<\/span><\/li>\n
                      4. Free-form data<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 2<\/b><\/p>\n

                        Explanation<\/b><\/h3>\n

                        Structured data is organized according to a predefined schema, commonly using tables containing rows and columns. Each column typically has an expected data type, such as integer, date, decimal, or text. Relational databases are a common example of structured data storage. Unstructured data includes formats such as images, audio, video, and free-form documents. Structured data is generally easier to query using SQL because its organization and field definitions are explicitly defined. Analysts should still validate the quality, meaning, and consistency of structured data before using it.<\/span><\/p>\n

                        Question 332<\/b><\/h3>\n

                        Which technique is commonly used to divide a dataset into training and testing portions when developing a predictive model?<\/b><\/p>\n

                          \n
                        1. Data splitting<\/span><\/li>\n
                        2. Data deletion<\/span><\/li>\n
                        3. Data aggregation<\/span><\/li>\n
                        4. Data encryption<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 1<\/b><\/p>\n

                          Explanation<\/b><\/h3>\n

                          Data splitting separates a dataset into subsets for different purposes, commonly training and testing. The training data is used to develop or fit a model, while the testing data is used to evaluate how the model performs on observations that were not used during training. Some workflows also use a validation dataset. Proper splitting helps reduce the risk of evaluating a model only on data it has already seen. Analysts should consider randomization, class balance, time ordering, and potential data leakage when designing a model evaluation process.<\/span><\/p>\n

                          Question 333<\/b><\/h3>\n

                          Which SQL keyword can be used to assign a temporary name to a selected column or expression in query results?<\/b><\/p>\n

                            \n
                          1. LABEL<\/span><\/li>\n
                          2. RENAME<\/span><\/li>\n
                          3. AS<\/span><\/li>\n
                          4. NAME<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 3<\/b><\/p>\n

                            Explanation<\/b><\/h3>\n

                            The AS keyword is commonly used to assign an alias to a column, expression, or table within a SQL query. For example, <\/span>SELECT SUM(sales) AS total_sales FROM orders<\/span> displays the calculated result using the alias <\/span>total_sales<\/span>. Aliases can make query results easier to understand and can simplify references to tables or complex expressions. The exact requirement for AS varies by database system in some contexts, but it is widely supported. Clear aliases are particularly useful when creating analytical reports or combining multiple calculated fields.<\/span><\/p>\n

                            Question 334<\/b><\/h3>\n

                            Which type of bias can occur when people who choose to participate in a survey differ systematically from those who do not participate?<\/b><\/p>\n

                              \n
                            1. Selection bias<\/span><\/li>\n
                            2. Calculation bias<\/span><\/li>\n
                            3. Storage bias<\/span><\/li>\n
                            4. Formatting bias<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 1<\/b><\/p>\n

                              Explanation<\/b><\/h3>\n

                              Selection bias occurs when the individuals included in a sample differ systematically from the population the analysis intends to represent. In surveys, this can happen when people who choose to respond have different characteristics or opinions from those who do not participate. As a result, the survey findings may not accurately represent the target population. Analysts should consider recruitment methods, response rates, sampling frames, and nonresponse patterns. Increasing sample size alone does not necessarily eliminate selection bias if the sampling process systematically excludes or overrepresents certain groups.<\/span><\/p>\n

                              Question 335<\/b><\/h3>\n

                              Which visualization is commonly used to show the proportion of a total represented by several categories at a single point in time?<\/b><\/p>\n

                                \n
                              1. Scatter plot<\/span><\/li>\n
                              2. Pie chart<\/span><\/li>\n
                              3. Line chart<\/span><\/li>\n
                              4. Histogram<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2<\/b><\/p>\n

                                Explanation<\/b><\/h3>\n

                                A pie chart displays categories as portions of a complete whole, making it useful when the goal is to communicate composition at a particular point in time. For example, it can show the percentage of total sales contributed by several product categories. Pie charts become difficult to interpret when there are many categories or when differences between similar-sized slices are important. Bar charts are often more suitable for precise comparisons. Scatter plots show numerical relationships, line charts show trends, and histograms show distributions.<\/span><\/p>\n

                                Question 336<\/b><\/h3>\n

                                Which concept refers to the ability to determine where a reported data value originated and how it was transformed?<\/b><\/p>\n

                                  \n
                                1. Data lineage<\/span><\/li>\n
                                2. Data compression<\/span><\/li>\n
                                3. Data duplication<\/span><\/li>\n
                                4. Data partitioning<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1<\/b><\/p>\n

                                  Explanation<\/b><\/h3>\n

                                  Data lineage provides information about the origin, movement, and transformation of data. It can show which source system produced a value, what transformations were applied, and where the resulting information was stored or displayed. Lineage is valuable for troubleshooting, auditing, impact analysis, governance, and validating reports. For example, if a dashboard KPI changes unexpectedly, lineage can help identify whether the source data, transformation logic, or reporting layer caused the change. Compression reduces storage requirements, duplication creates repeated information, and partitioning organizes stored data.<\/span><\/p>\n

                                  Question 337<\/b><\/h3>\n

                                  Which SQL clause can be used to return only records where a specified column contains a particular value?<\/b><\/p>\n

                                    \n
                                  1. GROUP BY<\/span><\/li>\n
                                  2. ORDER BY<\/span><\/li>\n
                                  3. WHERE<\/span><\/li>\n
                                  4. HAVING<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 3<\/b><\/p>\n

                                    Explanation<\/b><\/h3>\n

                                    The WHERE clause filters records according to specified conditions. For example, <\/span>WHERE status = ‘Active’<\/span> returns only records where the status column equals Active. Multiple conditions can be combined using logical operators such as AND and OR. WHERE normally filters individual rows before grouping or aggregation. GROUP BY creates groups, ORDER BY sorts results, and HAVING filters grouped results. Accurate filtering is important because an incorrect condition can exclude relevant records or include records that should not be part of the analysis.<\/span><\/p>\n

                                    Question 338<\/b><\/h3>\n

                                    Which measure of dispersion is the square root of variance?<\/b><\/p>\n

                                      \n
                                    1. Range<\/span><\/li>\n
                                    2. Standard deviation<\/span><\/li>\n
                                    3. Median<\/span><\/li>\n
                                    4. Percentile<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2<\/b><\/p>\n

                                      Explanation<\/b><\/h3>\n

                                      Standard deviation is the square root of variance. Taking the square root converts the squared units of variance back into the original measurement units, making standard deviation easier to interpret. For example, if a dataset measures income in dollars, variance is expressed in squared dollars, while standard deviation is expressed in dollars. Range measures the difference between maximum and minimum values, median measures central position, and percentile measures relative position. Standard deviation is commonly used to describe how widely observations are dispersed around the mean.<\/span><\/p>\n

                                      Question 339<\/b><\/h3>\n

                                      Which data governance practice establishes who is responsible for managing a particular dataset or data domain?<\/b><\/p>\n

                                        \n
                                      1. Data ownership<\/span><\/li>\n
                                      2. Data visualization<\/span><\/li>\n
                                      3. Data aggregation<\/span><\/li>\n
                                      4. Data compression<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 1<\/b><\/p>\n

                                        Explanation<\/b><\/h3>\n

                                        Data ownership establishes responsibility for a dataset or data domain and helps define who has authority over decisions concerning its use, quality, access, and management. An owner may work with data stewards and technical teams to establish definitions, quality standards, access requirements, and governance procedures. Visualization focuses on presenting information, aggregation summarizes data, and compression reduces storage size. Clear ownership is important because datasets without defined responsibility can experience inconsistent definitions, unresolved quality problems, or unclear access decisions.<\/span><\/p>\n

                                        Question 340<\/b><\/h3>\n

                                        Which method can be used to compare actual performance against a predefined target or threshold?<\/b><\/p>\n

                                          \n
                                        1. KPI analysis<\/span><\/li>\n
                                        2. Data masking<\/span><\/li>\n
                                        3. Data deduplication<\/span><\/li>\n
                                        4. Schema migration<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 1<\/b><\/p>\n

                                          Explanation<\/b><\/h3>\n

                                          KPI analysis compares measured performance against defined objectives, targets, thresholds, or benchmarks. For example, an organization might compare an actual customer satisfaction score with its target score or compare monthly revenue with a planned target. This allows stakeholders to identify performance gaps and investigate potential causes. KPIs should have clearly defined formulas, data sources, time periods, and target values. Data masking protects sensitive information, deduplication removes repeated records, and schema migration changes database structures. Consistent KPI definitions are essential when performance is compared across teams or reporting periods.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full CompTIA Data+ DA0-002 Exam Dumps and Practice Test Dumps. Question 321 Which statistical measure identifies the middle value when a dataset is arranged in ascending or descending order? Mode Mean Median Variance Correct Answer: 3 Explanation The median is the middle value of an ordered dataset. If there is an odd number of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15980"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=15980"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15980\/revisions"}],"predecessor-version":[{"id":15990,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15980\/revisions\/15990"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=15980"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=15980"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=15980"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}