{"id":15981,"date":"2026-09-18T10:40:20","date_gmt":"2026-09-18T10:40:20","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=15981"},"modified":"2026-09-18T10:40:20","modified_gmt":"2026-09-18T10:40:20","slug":"comptia-data-da0-002-practice-test-questions-and-exam-dumps-part18-q341-360","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/comptia-data-da0-002-practice-test-questions-and-exam-dumps-part18-q341-360\/","title":{"rendered":"CompTIA Data+ DA0-002 Practice Test Questions and Exam Dumps Part18 Q341-360"},"content":{"rendered":"

<\/h1>\n

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

Question 341<\/b><\/h3>\n

Which SQL function is commonly used to count the number of non-NULL values in a specific column?<\/b><\/p>\n

    \n
  1. SUM()<\/span><\/li>\n
  2. COUNT(column)<\/span><\/li>\n
  3. AVG()<\/span><\/li>\n
  4. MAX()<\/span><\/li>\n<\/ol>\n

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

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

    COUNT(column)<\/span> counts the non-NULL values in the specified column. This differs from <\/span>COUNT(*)<\/span>, which counts rows regardless of whether individual columns contain NULL values. For example, <\/span>COUNT(email)<\/span> can determine how many records have an email value populated. SUM() calculates a numerical total, AVG() calculates an average, and MAX() identifies the largest value. Understanding the distinction between <\/span>COUNT(*)<\/span> and <\/span>COUNT(column)<\/span> is important when analyzing completeness because missing values can affect the resulting count. Analysts should also use <\/span>COUNT(DISTINCT column)<\/span> when they need the number of unique non-NULL values.<\/span><\/p>\n

    Question 342<\/b><\/h3>\n

    Which data quality dimension determines whether each record represents a single unique entity without unintended duplication?<\/b><\/p>\n

      \n
    1. Accuracy<\/span><\/li>\n
    2. Timeliness<\/span><\/li>\n
    3. Uniqueness<\/span><\/li>\n
    4. Completeness<\/span><\/li>\n<\/ol>\n

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

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

      Uniqueness indicates whether records are free from unintended duplication. For example, if a customer should have one master record but appears multiple times because of repeated imports, the dataset has a uniqueness problem. Duplicate records can distort counts, totals, customer metrics, and other analytical results. Accuracy concerns whether information is correct, timeliness concerns whether information is current, and completeness concerns whether required information is present. Analysts can use unique identifiers, duplicate checks, matching rules, and deduplication processes to improve uniqueness before performing aggregations or generating reports.<\/span><\/p>\n

      Question 343<\/b><\/h3>\n

      Which SQL statement is used to retrieve only rows where a column does not contain a NULL value?<\/b><\/p>\n

        \n
      1. WHERE column IS NOT NULL<\/span><\/li>\n
      2. WHERE column != NULL<\/span><\/li>\n
      3. WHERE column NOT NULL<\/span><\/li>\n
      4. WHERE column <> EMPTY<\/span><\/li>\n<\/ol>\n

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

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

        SQL uses <\/span>IS NOT NULL<\/span> to test whether a value is present rather than NULL. For example, <\/span>WHERE email IS NOT NULL<\/span> returns rows where the email field contains a non-NULL value. Comparisons such as <\/span>column != NULL<\/span> do not correctly test for NULL because NULL represents an unknown or missing value and requires special SQL comparison syntax. Correct NULL handling is important when analyzing completeness, calculating metrics, or filtering datasets. Analysts should also distinguish between NULL, an empty string, and a valid value because database systems may treat these differently.<\/span><\/p>\n

        Question 344<\/b><\/h3>\n

        Which type of data analysis summarizes information using measures such as totals, averages, counts, and percentages?<\/b><\/p>\n

          \n
        1. Predictive analysis<\/span><\/li>\n
        2. Prescriptive analysis<\/span><\/li>\n
        3. Descriptive analysis<\/span><\/li>\n
        4. Diagnostic analysis<\/span><\/li>\n<\/ol>\n

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

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

          Descriptive analysis summarizes data to explain what has happened or what is currently observed. Common techniques include counts, sums, averages, percentages, distributions, and trend summaries. For example, a monthly sales report showing total revenue, average order value, and number of transactions is descriptive analysis. Diagnostic analysis investigates why an outcome occurred, predictive analysis estimates future outcomes, and prescriptive analysis evaluates possible actions. Descriptive analysis is often the foundation for more advanced analysis because analysts first need an accurate understanding of the available data and historical patterns.<\/span><\/p>\n

          Question 345<\/b><\/h3>\n

          Which chart is most appropriate for displaying the frequency distribution of a continuous numerical variable?<\/b><\/p>\n

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

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

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

            A histogram displays the frequency distribution of continuous or numerical data by dividing values into intervals called bins. The height of each bin represents the number or proportion of observations falling within that interval. Histograms can help reveal the shape of a distribution, including skewness, concentration, gaps, and multiple peaks. A pie chart is used for proportions, a bar chart is typically used for discrete categories, and a line chart is commonly used for trends. Analysts should select sensible bin widths because very narrow or very wide bins can obscure meaningful patterns.<\/span><\/p>\n

            Question 346<\/b><\/h3>\n

            Which database design concept reduces unnecessary duplication of data by organizing information into related tables?<\/b><\/p>\n

              \n
            1. Normalization<\/span><\/li>\n
            2. Aggregation<\/span><\/li>\n
            3. Visualization<\/span><\/li>\n
            4. Encryption<\/span><\/li>\n<\/ol>\n

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

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

              Database normalization organizes data into related tables to reduce unnecessary duplication and improve data integrity. Instead of storing the same customer information repeatedly in every transaction record, a normalized design can store customer details in one table and transactions in another connected through a key. Normalization can reduce update anomalies and make relationships clearer. Aggregation summarizes data, visualization presents information graphically, and encryption protects information from unauthorized access. Excessive normalization can sometimes make analytical queries more complex, so database design should consider both data integrity and operational or analytical requirements.<\/span><\/p>\n

              Question 347<\/b><\/h3>\n

              Which measure is calculated as the sum of all observations divided by the number of observations?<\/b><\/p>\n

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

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

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

                The mean, or arithmetic average, is calculated by adding all observations and dividing the total by the number of observations. For example, the mean of 10, 20, and 30 is 20. The mean is widely used to summarize numerical data but can be strongly influenced by extreme values. The median identifies the middle position, the mode identifies the most frequent value, and a percentile indicates relative position within a distribution. Analysts should examine the distribution and potential outliers before deciding whether the mean is an appropriate summary statistic.<\/span><\/p>\n

                Question 348<\/b><\/h3>\n

                Which SQL operation combines rows from two tables based on a related column or condition?<\/b><\/p>\n

                  \n
                1. FILTER<\/span><\/li>\n
                2. JOIN<\/span><\/li>\n
                3. SORT<\/span><\/li>\n
                4. GROUP<\/span><\/li>\n<\/ol>\n

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

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

                  A JOIN combines rows from two or more tables based on a related field or specified condition. For example, a customer table can be joined with an orders table using CustomerID. SQL provides several join types, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN, each determining which matching and unmatched records are retained. Joining data requires compatible keys and careful consideration of duplicate relationships. Incorrect joins can create duplicated rows or missing information, which may produce inaccurate analytical results. Analysts should validate join results before using them in calculations.<\/span><\/p>\n

                  Question 349<\/b><\/h3>\n

                  Which data collection source is most likely to provide measurements such as temperature, pressure, or machine vibration automatically?<\/b><\/p>\n

                    \n
                  1. Sensor data<\/span><\/li>\n
                  2. Survey responses<\/span><\/li>\n
                  3. Manual interview notes<\/span><\/li>\n
                  4. Customer reviews<\/span><\/li>\n<\/ol>\n

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

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

                    Sensor data is automatically generated by devices that measure physical or environmental conditions. Examples include temperature sensors, pressure sensors, motion detectors, and industrial equipment monitoring systems. Sensor data can provide frequent or real-time observations and is commonly used in manufacturing, logistics, healthcare, environmental monitoring, and IoT systems. Because sensors can generate large volumes of information, analysts may need to handle timestamps, missing measurements, abnormal readings, and calibration issues. Survey responses and interviews depend on human input, while customer reviews generally contain manually or voluntarily submitted text.<\/span><\/p>\n

                    Question 350<\/b><\/h3>\n

                    Which technique helps identify whether a numerical observation is unusually far from the rest of the dataset by using the interquartile range?<\/b><\/p>\n

                      \n
                    1. Mean comparison<\/span><\/li>\n
                    2. IQR method<\/span><\/li>\n
                    3. Frequency encoding<\/span><\/li>\n
                    4. Data masking<\/span><\/li>\n<\/ol>\n

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

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

                      The interquartile range, or IQR, is the difference between the third quartile (Q3) and first quartile (Q1). A common outlier detection rule identifies observations below Q1 \u2212 1.5 \u00d7 IQR or above Q3 + 1.5 \u00d7 IQR as potential outliers. This method is useful because it is less influenced by extreme values than methods based directly on the mean and standard deviation. Potential outliers should be investigated rather than automatically deleted because they may represent valid rare events, data-entry errors, or other meaningful observations.<\/span><\/p>\n

                      Question 351<\/b><\/h3>\n

                      Which SQL keyword removes duplicate rows from a query result when used with SELECT?<\/b><\/p>\n

                        \n
                      1. UNIQUE<\/span><\/li>\n
                      2. DISTINCT<\/span><\/li>\n
                      3. DEDUP<\/span><\/li>\n
                      4. SINGLE<\/span><\/li>\n<\/ol>\n

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

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

                        The DISTINCT keyword removes duplicate combinations of selected column values from the result of a SELECT query. For example, <\/span>SELECT DISTINCT city FROM customers<\/span> returns each city once even if multiple customers belong to the same city. DISTINCT affects the query result rather than necessarily modifying the underlying table. If multiple columns are selected, uniqueness is evaluated across the combination of those columns. Analysts should distinguish DISTINCT from database-level deduplication because DISTINCT does not remove duplicate records from the source table.<\/span><\/p>\n

                        Question 352<\/b><\/h3>\n

                        Which principle requires an organization to restrict access to sensitive data according to a user’s authorized role and responsibilities?<\/b><\/p>\n

                          \n
                        1. Role-based access control<\/span><\/li>\n
                        2. Data aggregation<\/span><\/li>\n
                        3. Data normalization<\/span><\/li>\n
                        4. Data visualization<\/span><\/li>\n<\/ol>\n

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

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

                          Role-based access control, or RBAC, assigns permissions according to defined user roles. For example, a financial analyst may receive access to financial reporting data while a customer-service representative may receive access to customer records required for support. RBAC helps organizations apply consistent access policies and supports least-privilege principles. Data aggregation summarizes information, normalization organizes data, and visualization presents information. Access roles should be reviewed regularly because users may change responsibilities. Sensitive datasets should also be protected through appropriate authentication, authorization, monitoring, and other security controls.<\/span><\/p>\n

                          Question 353<\/b><\/h3>\n

                          Which statistical concept describes a range of plausible values for a population parameter based on sample data and a specified confidence level?<\/b><\/p>\n

                            \n
                          1. Frequency distribution<\/span><\/li>\n
                          2. Confidence interval<\/span><\/li>\n
                          3. Mode<\/span><\/li>\n
                          4. Range<\/span><\/li>\n<\/ol>\n

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

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

                            A confidence interval provides a range of values calculated from sample data that is intended to capture a population parameter with a specified confidence level under the assumptions of the statistical method. For example, an analyst may report a confidence interval around an estimated population mean. A confidence interval communicates uncertainty rather than providing a guarantee that the parameter falls inside the particular calculated interval. The exact interpretation depends on the statistical framework and repeated-sampling concept. Analysts should report the confidence level, estimation method, and relevant assumptions when presenting intervals.<\/span><\/p>\n

                            Question 354<\/b><\/h3>\n

                            Which SQL command is used to modify existing data values in a table?<\/b><\/p>\n

                              \n
                            1. ALTER<\/span><\/li>\n
                            2. INSERT<\/span><\/li>\n
                            3. UPDATE<\/span><\/li>\n
                            4. SELECT<\/span><\/li>\n<\/ol>\n

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

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

                              UPDATE changes existing values in rows within a table. An UPDATE statement commonly specifies the columns to change and uses a WHERE clause to identify the records that should be modified. For example, <\/span>UPDATE customers SET status = ‘Inactive’ WHERE customer_id = 1001<\/span> changes the status for the specified customer. INSERT adds new rows, ALTER changes table structure, and SELECT retrieves information. Because an UPDATE without a sufficiently restrictive WHERE clause can affect many records, analysts and database administrators should test and verify update conditions carefully.<\/span><\/p>\n

                              Question 355<\/b><\/h3>\n

                              Which metric measures the percentage of records in a dataset that contain a value for a required field?<\/b><\/p>\n

                                \n
                              1. Completeness rate<\/span><\/li>\n
                              2. Correlation rate<\/span><\/li>\n
                              3. Variance rate<\/span><\/li>\n
                              4. Accuracy rate<\/span><\/li>\n<\/ol>\n

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

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

                                A completeness rate measures how much of the required information is populated. For example, if 950 out of 1,000 customer records contain a required phone number, the completeness rate for that field is 95%. Completeness does not determine whether the populated values are correct; a field can be complete but inaccurate. Analysts should define which fields are required and how missing, NULL, or invalid values are treated when calculating completeness. Monitoring completeness over time can help identify problems in data collection, system integration, or data-entry processes.<\/span><\/p>\n

                                Question 356<\/b><\/h3>\n

                                Which analytical technique is commonly used to estimate how a dependent variable changes based on one or more independent variables?<\/b><\/p>\n

                                  \n
                                1. Clustering<\/span><\/li>\n
                                2. Regression<\/span><\/li>\n
                                3. Deduplication<\/span><\/li>\n
                                4. Aggregation<\/span><\/li>\n<\/ol>\n

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

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

                                  Regression analysis models the relationship between a dependent variable and one or more independent variables. For example, an analyst might use regression to examine how sales relate to advertising expenditure, price, seasonality, and other factors. Regression can be used for explanation, prediction, and estimation depending on the model and objective. Clustering groups observations based on similarity, deduplication removes repeated records, and aggregation summarizes information. Analysts should examine assumptions, potential confounding variables, outliers, model performance, and the appropriate type of regression before interpreting results.<\/span><\/p>\n

                                  Question 357<\/b><\/h3>\n

                                  Which data format is commonly used by web APIs to exchange structured or semi-structured information using objects and arrays?<\/b><\/p>\n

                                    \n
                                  1. JSON<\/span><\/li>\n
                                  2. CSV<\/span><\/li>\n
                                  3. Plain text<\/span><\/li>\n
                                  4. Fixed-width text<\/span><\/li>\n<\/ol>\n

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

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

                                    JSON is widely used by web APIs because it can represent structured and semi-structured information through objects, key-value pairs, and arrays. For example, an API response may return customer details along with an array of recent orders. JSON is relatively lightweight and supported by many programming languages and data-processing tools. CSV is primarily tabular and does not naturally support nested structures, while plain-text and fixed-width formats are less suitable for hierarchical API responses. Analysts working with API data may need to parse nested objects and arrays before loading the information into analytical systems.<\/span><\/p>\n

                                    Question 358<\/b><\/h3>\n

                                    Which type of sampling divides a population into subgroups and then selects samples from each subgroup?<\/b><\/p>\n

                                      \n
                                    1. Convenience sampling<\/span><\/li>\n
                                    2. Stratified sampling<\/span><\/li>\n
                                    3. Snowball sampling<\/span><\/li>\n
                                    4. Systematic error<\/span><\/li>\n<\/ol>\n

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

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

                                      Stratified sampling divides a population into defined subgroups, called strata, and then selects observations from each subgroup. For example, an organization might divide customers by region and sample customers from each region to ensure geographic representation. This approach can be useful when important subgroups might otherwise be underrepresented in a simple random sample. Convenience sampling selects easily accessible participants, while snowball sampling relies on participant referrals. Stratified sampling requires clearly defined strata and an appropriate sampling strategy within each group. Analysts should document how the strata and sample sizes were determined.<\/span><\/p>\n

                                      Question 359<\/b><\/h3>\n

                                      Which data governance document typically provides standardized definitions for business terms such as \u201cactive customer\u201d or \u201cnet revenue\u201d?<\/b><\/p>\n

                                        \n
                                      1. Business glossary<\/span><\/li>\n
                                      2. Transaction log<\/span><\/li>\n
                                      3. Access control list<\/span><\/li>\n
                                      4. Backup catalog<\/span><\/li>\n<\/ol>\n

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

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

                                        A business glossary provides standardized definitions for important business terms and concepts. For example, it can define exactly what the organization means by “active customer,” “net revenue,” or “churned customer.” This helps different departments use consistent terminology and reduces disagreements about KPI calculations. A transaction log records system events, an access control list defines permissions, and a backup catalog helps track backup information. A business glossary can be connected with data governance and metadata management so users can understand both the business meaning and technical implementation of important data elements.<\/span><\/p>\n

                                        Question 360<\/b><\/h3>\n

                                        Which measure is particularly useful for comparing the relative variability of datasets that have different units or substantially different means?<\/b><\/p>\n

                                          \n
                                        1. Range<\/span><\/li>\n
                                        2. Variance<\/span><\/li>\n
                                        3. Coefficient of variation<\/span><\/li>\n
                                        4. Median<\/span><\/li>\n<\/ol>\n

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

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

                                          The coefficient of variation (CV) measures relative variability by comparing the standard deviation with the mean, commonly expressed as a percentage. It can help compare the relative dispersion of datasets measured on different scales or with different means, provided the mean is meaningful and appropriate for the calculation. Range depends only on the minimum and maximum values, variance uses squared units, and median describes central position. Analysts should be cautious when the mean is near zero because the coefficient of variation can become unstable or difficult to interpret in such situations.<\/span><\/p>\n

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

                                          View Full CompTIA Data+ DA0-002 Exam Dumps and Practice Test Dumps. Question 341 Which SQL function is commonly used to count the number of non-NULL values in a specific column? SUM() COUNT(column) AVG() MAX() Correct Answer: 2 Explanation COUNT(column) counts the non-NULL values in the specified column. This differs from COUNT(*), which counts rows regardless […]<\/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\/15981"}],"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=15981"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15981\/revisions"}],"predecessor-version":[{"id":15989,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/15981\/revisions\/15989"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=15981"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=15981"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=15981"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}