{"id":17811,"date":"2026-09-21T11:36:29","date_gmt":"2026-09-21T11:36:29","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=17811"},"modified":"2026-09-21T11:36:29","modified_gmt":"2026-09-21T11:36:29","slug":"databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part5-q81-100","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part5-q81-100\/","title":{"rendered":"Databricks Certified Associate Developer for Apache Spark Practice Test Questions and Exam Dumps Part5 Q81-100"},"content":{"rendered":"

View Full <\/b>Databricks Certified Associate Developer for Apache Spark Exam Dumps <\/b><\/a>\u00a0and Practice Test Dumps<\/b><\/h2>\n

 <\/p>\n

Question 81.<\/b><\/p>\n

Which DataFrame method is used to inspect the schema of a DataFrame in a tree-like format?<\/b><\/p>\n

    \n
  1. <\/b> printSchema()<\/span>
    \n<\/span>2.<\/b> describe()<\/span>
    \n<\/span>3.<\/b> explain()<\/span>
    \n<\/span>4.<\/b> summary()<\/span><\/li>\n<\/ol>\n

    Correct Answer: 1. printSchema()<\/b><\/p>\n

    Explanation:<\/b><\/p>\n

    printSchema() displays the structure of a DataFrame, including column names, data types, nullability, and nested fields. It is especially useful when working with complex schemas containing arrays, maps, or structs. describe() and summary() produce statistics, while explain() displays logical and physical execution plans. printSchema() is therefore the appropriate method when a developer wants to quickly verify how Spark interpreted or constructed the structure of a dataset.<\/span><\/p>\n

    Question 82.<\/b><\/p>\n

    Which DataFrame property returns the schema as a StructType object?<\/b><\/p>\n

      \n
    1. <\/b> columns<\/span>
      \n<\/span>2.<\/b> schema<\/span>
      \n<\/span>3.<\/b> dtypes<\/span>
      \n<\/span>4.<\/b> storageLevel<\/span><\/li>\n<\/ol>\n

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

      Explanation:<\/b><\/p>\n

      The schema property returns a StructType describing the DataFrame structure. StructType contains StructField objects that define each column’s name, data type, nullability, and optional metadata. columns returns only column names, dtypes provides name-and-type pairs in a simpler format, and storageLevel describes persistence behavior. Accessing the schema programmatically is useful when validating datasets, comparing structures, or dynamically constructing transformation logic.<\/span><\/p>\n

      Question 83.<\/b><\/p>\n

      Which DataFrame property returns a list containing the names of all columns?<\/b><\/p>\n

        \n
      1. <\/b> dtypes<\/span>
        \n<\/span>2.<\/b> schema<\/span>
        \n<\/span>3.<\/b> columns<\/span>
        \n<\/span>4.<\/b> printSchema<\/span><\/li>\n<\/ol>\n

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

        Explanation:<\/b><\/p>\n

        The columns property returns a Python list containing the names of all DataFrame columns in their current order. This is useful when dynamically selecting, renaming, validating, or iterating over columns. schema returns the full StructType, dtypes returns column names with type strings, and printSchema() displays the schema rather than returning just the names. columns is therefore the simplest choice when only the column-name list is needed.<\/span><\/p>\n

        Question 84.<\/b><\/p>\n

        Which method can return the data types of DataFrame columns as name-type pairs?<\/b><\/p>\n

          \n
        1. <\/b> printSchema()<\/span>
          \n<\/span>2.<\/b> columns<\/span>
          \n<\/span>3.<\/b> explain()<\/span>
          \n<\/span>4.<\/b> dtypes<\/span><\/li>\n<\/ol>\n

          Correct Answer: 4. dtypes<\/b><\/p>\n

          Explanation:<\/b><\/p>\n

          dtypes returns a list of tuples containing each column name and its corresponding data type represented as a string. It provides a compact way to inspect column types programmatically. printSchema() displays a formatted schema, columns returns only names, and explain() shows execution plans. dtypes is helpful for quick validation or for building logic that depends on whether columns are strings, numeric values, timestamps, or other Spark SQL types.<\/span><\/p>\n

          Question 85.<\/b><\/p>\n

          Which Spark SQL function is commonly used to convert a string column to uppercase?<\/b><\/p>\n

            \n
          1. <\/b> upper()<\/span>
            \n<\/span>2.<\/b> trim()<\/span>
            \n<\/span>3.<\/b> initcap()<\/span>
            \n<\/span>4.<\/b> regexp_replace()<\/span><\/li>\n<\/ol>\n

            Correct Answer: 1. upper()<\/b><\/p>\n

            Explanation:<\/b><\/p>\n

            upper() converts alphabetic characters in a string expression to uppercase. It is frequently used to normalize text before comparisons, joins, grouping, or deduplication. trim() removes leading and trailing whitespace, initcap() capitalizes words, and regexp_replace() performs regular-expression substitutions. Standardizing case can reduce mismatches caused by inconsistent capitalization across different source systems.<\/span><\/p>\n

            Question 86.<\/b><\/p>\n

            Which function returns the absolute value of a numeric expression?<\/b><\/p>\n

              \n
            1. <\/b> round()<\/span>
              \n<\/span>2.<\/b> abs()<\/span>
              \n<\/span>3.<\/b> ceil()<\/span>
              \n<\/span>4.<\/b> floor()<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2. abs()<\/b><\/p>\n

              Explanation:<\/b><\/p>\n

              abs() returns the absolute value of a numeric expression, converting negative values to their positive magnitude while leaving positive values unchanged. round() rounds a value, ceil() returns the smallest integer greater than or equal to a value, and floor() returns the largest integer less than or equal to it. abs() is useful in calculations involving distance, differences, deviations, and other cases where sign should be ignored.<\/span><\/p>\n

              Question 87.<\/b><\/p>\n

              Which Spark SQL function rounds a numeric value to a specified number of decimal places?<\/b><\/p>\n

                \n
              1. <\/b> ceil()<\/span>
                \n<\/span>2.<\/b> floor()<\/span>
                \n<\/span>3.<\/b> round()<\/span>
                \n<\/span>4.<\/b> abs()<\/span><\/li>\n<\/ol>\n

                Correct Answer: 3. round()<\/b><\/p>\n

                Explanation:<\/b><\/p>\n

                round() rounds numeric values to a specified number of decimal places. It is commonly used in reporting, financial calculations, and data preparation when excessive precision is unnecessary. ceil() rounds upward to an integer boundary, floor() rounds downward, and abs() returns absolute magnitude. Spark’s round() function can be applied directly to column expressions as part of a select() or withColumn() transformation.<\/span><\/p>\n

                Question 88.<\/b><\/p>\n

                Which function returns the smallest integer greater than or equal to a numeric value?<\/b><\/p>\n

                  \n
                1. <\/b> floor()<\/span>
                  \n<\/span>2.<\/b> round()<\/span>
                  \n<\/span>3.<\/b> abs()<\/span>
                  \n<\/span>4.<\/b> ceil()<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 4. ceil()<\/b><\/p>\n

                  Explanation:<\/b><\/p>\n

                  ceil() returns the smallest integer value that is greater than or equal to the input. For example, ceil(4.2) returns 5. floor() performs the opposite directional rounding, round() rounds according to standard rounding rules, and abs() returns the magnitude of a number. ceil() is useful when calculations must round upward, such as determining required containers, batches, or resource counts.<\/span><\/p>\n

                  Question 89.<\/b><\/p>\n

                  Which function returns the largest integer less than or equal to a numeric value?<\/b><\/p>\n

                    \n
                  1. <\/b> floor()<\/span>
                    \n<\/span>2.<\/b> ceil()<\/span>
                    \n<\/span>3.<\/b> round()<\/span>
                    \n<\/span>4.<\/b> abs()<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. floor()<\/b><\/p>\n

                    Explanation:<\/b><\/p>\n

                    floor() returns the largest integer that is less than or equal to a numeric value. For example, floor(4.9) returns 4. ceil() rounds upward, round() applies normal rounding behavior, and abs() removes the sign. floor() is useful when a calculation needs to discard the fractional portion while ensuring that positive values are rounded downward.<\/span><\/p>\n

                    Question 90.<\/b><\/p>\n

                    Which Spark SQL function can extract the year from a date or timestamp column?<\/b><\/p>\n

                      \n
                    1. <\/b> date_format()<\/span>
                      \n<\/span>2.<\/b> year()<\/span>
                      \n<\/span>3.<\/b> to_date()<\/span>
                      \n<\/span>4.<\/b> current_date()<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2. year()<\/b><\/p>\n

                      Explanation:<\/b><\/p>\n

                      year() extracts the year component from a date or timestamp expression. It is commonly used for grouping, filtering, or creating derived calendar fields. date_format() formats a date or timestamp as a string, to_date() converts compatible values to a date, and current_date() returns the current date. year() is therefore the direct choice when only the year component is required.<\/span><\/p>\n

                      Question 91.<\/b><\/p>\n

                      Which function converts a compatible string or timestamp expression into a DateType value?<\/b><\/p>\n

                        \n
                      1. <\/b> current_date()<\/span>
                        \n<\/span>2.<\/b> date_format()<\/span>
                        \n<\/span>3.<\/b> to_date()<\/span>
                        \n<\/span>4.<\/b> year()<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 3. to_date()<\/b><\/p>\n

                        Explanation:<\/b><\/p>\n

                        to_date() converts a compatible string, timestamp, or other supported expression into Spark’s DateType. An optional format can be provided when parsing strings that do not use the default expected structure. current_date() produces today’s date, date_format() converts dates or timestamps to formatted strings, and year() extracts the year component. to_date() is commonly used when preparing textual date fields for proper date-based filtering and calculations.<\/span><\/p>\n

                        Question 92.<\/b><\/p>\n

                        Which Spark SQL function returns the current date?<\/b><\/p>\n

                          \n
                        1. <\/b> to_date()<\/span>
                          \n<\/span>2.<\/b> year()<\/span>
                          \n<\/span>3.<\/b> date_format()<\/span>
                          \n<\/span>4.<\/b> current_date()<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 4. current_date()<\/b><\/p>\n

                          Explanation:<\/b><\/p>\n

                          current_date() returns the current date as a Spark SQL DateType expression. It can be used in DataFrame transformations, filtering, or calculations such as determining record age. to_date() converts other expressions to DateType, year() extracts a year, and date_format() converts dates into formatted strings. current_date() is especially useful when adding processing dates or comparing records against the date on which a Spark job runs.<\/span><\/p>\n

                          Question 93.<\/b><\/p>\n

                          Which function returns the current timestamp in Spark SQL?<\/b><\/p>\n

                            \n
                          1. <\/b> current_timestamp()<\/span>
                            \n<\/span>2.<\/b> current_date()<\/span>
                            \n<\/span>3.<\/b> unix_timestamp()<\/span>
                            \n<\/span>4.<\/b> to_timestamp()<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1. current_timestamp()<\/b><\/p>\n

                            Explanation:<\/b><\/p>\n

                            current_timestamp() returns the current date and time as a timestamp expression. It is commonly used to add processing timestamps, audit fields, or ingestion metadata. current_date() returns only a date, to_timestamp() converts compatible expressions into timestamp values, and unix_timestamp() is associated with Unix-time conversion. current_timestamp() is therefore appropriate when both date and time are required.<\/span><\/p>\n

                            Question 94.<\/b><\/p>\n

                            Which function converts a compatible string into a TimestampType value?<\/b><\/p>\n

                              \n
                            1. <\/b> current_timestamp()<\/span>
                              \n<\/span>2.<\/b> to_timestamp()<\/span>
                              \n<\/span>3.<\/b> date_format()<\/span>
                              \n<\/span>4.<\/b> year()<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 2. to_timestamp()<\/b><\/p>\n

                              Explanation:<\/b><\/p>\n

                              to_timestamp() converts a compatible string or expression into Spark’s TimestampType. A format can be specified when the input string uses a particular date-time pattern. current_timestamp() returns the current time, date_format() outputs formatted strings, and year() extracts only the year component. Converting textual timestamps to a true timestamp type enables proper chronological comparisons, window operations, and time-based calculations.<\/span><\/p>\n

                              Question 95.<\/b><\/p>\n

                              Which Spark SQL function is commonly used to format a date or timestamp as a string?<\/b><\/p>\n

                                \n
                              1. <\/b> to_date()<\/span>
                                \n<\/span>2.<\/b> current_date()<\/span>
                                \n<\/span>3.<\/b> date_format()<\/span>
                                \n<\/span>4.<\/b> datediff()<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 3. date_format()<\/b><\/p>\n

                                Explanation:<\/b><\/p>\n

                                date_format() converts a date or timestamp expression into a string according to a specified pattern. It is useful when generating display-friendly date fields, report labels, or formatted partition keys. to_date() converts expressions into DateType, current_date() returns today’s date, and datediff() calculates the number of days between dates. date_format() is intended for formatting rather than date arithmetic.<\/span><\/p>\n

                                Question 96.<\/b><\/p>\n

                                Which function returns the number of days between two dates?<\/b><\/p>\n

                                  \n
                                1. <\/b> months_between()<\/span>
                                  \n<\/span>2.<\/b> date_add()<\/span>
                                  \n<\/span>3.<\/b> date_sub()<\/span>
                                  \n<\/span>4.<\/b> datediff()<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 4. datediff()<\/b><\/p>\n

                                  Explanation:<\/b><\/p>\n

                                  datediff() calculates the difference in days between two date expressions. It is commonly used to measure elapsed time, record age, processing delays, or customer activity intervals. date_add() and date_sub() shift dates forward or backward, while months_between() calculates a month-based difference. datediff() is therefore the appropriate function when the required result is expressed as a number of days.<\/span><\/p>\n

                                  Question 97.<\/b><\/p>\n

                                  Which function can add a specified number of days to a date?<\/b><\/p>\n

                                    \n
                                  1. <\/b> date_add()<\/span>
                                    \n<\/span>2.<\/b> datediff()<\/span>
                                    \n<\/span>3.<\/b> months_between()<\/span>
                                    \n<\/span>4.<\/b> date_format()<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1. date_add()<\/b><\/p>\n

                                    Explanation:<\/b><\/p>\n

                                    date_add() returns a date shifted forward by the specified number of days. It is useful for calculating due dates, expiration dates, reporting windows, or future thresholds. datediff() measures the difference between dates, months_between() calculates a month-based difference, and date_format() converts dates into strings. date_add() provides a simple way to perform day-based date arithmetic in Spark.<\/span><\/p>\n

                                    Question 98.<\/b><\/p>\n

                                    Which function subtracts a specified number of days from a date?<\/b><\/p>\n

                                      \n
                                    1. <\/b> date_add()<\/span>
                                      \n<\/span>2.<\/b> date_sub()<\/span>
                                      \n<\/span>3.<\/b> datediff()<\/span>
                                      \n<\/span>4.<\/b> trunc()<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2. date_sub()<\/b><\/p>\n

                                      Explanation:<\/b><\/p>\n

                                      date_sub() returns a date that is a specified number of days earlier than the input date. It is useful for calculating historical boundaries, rolling windows, or prior-date comparisons. date_add() moves a date forward, datediff() returns the number of days between dates, and trunc() truncates dates to broader units such as month or year. date_sub() is therefore the direct function for backward day arithmetic.<\/span><\/p>\n

                                      Question 99.<\/b><\/p>\n

                                      Which Spark SQL function can calculate the number of months between two date or timestamp expressions?<\/b><\/p>\n

                                        \n
                                      1. <\/b> datediff()<\/span>
                                        \n<\/span>2.<\/b> add_months()<\/span>
                                        \n<\/span>3.<\/b> months_between()<\/span>
                                        \n<\/span>4.<\/b> date_sub()<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 3. months_between()<\/b><\/p>\n

                                        Explanation:<\/b><\/p>\n

                                        months_between() calculates the number of months between two date or timestamp values. It can return fractional months depending on the exact dates involved. datediff() returns a difference in days, add_months() shifts a date by a number of months, and date_sub() subtracts days. months_between() is useful for age calculations, subscription durations, billing analysis, and other month-based comparisons.<\/span><\/p>\n

                                        Question 100.<\/b><\/p>\n

                                        Which Spark SQL function can shift a date by a specified number of months?<\/b><\/p>\n

                                          \n
                                        1. <\/b> date_add()<\/span>
                                          \n<\/span>2.<\/b> months_between()<\/span>
                                          \n<\/span>3.<\/b> datediff()<\/span>
                                          \n<\/span>4.<\/b> add_months()<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 4. add_months()<\/b><\/p>\n

                                          Explanation:<\/b><\/p>\n

                                          add_months() shifts a date by a specified number of months. Positive values move the date forward, while negative values can move it backward. date_add() operates in days, months_between() calculates the difference between two dates in months, and datediff() returns a day-based difference. add_months() is particularly useful for monthly billing cycles, renewal schedules, forecasting, and other calendar-based transformations.<\/span><\/p>\n

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

                                          View Full Databricks Certified Associate Developer for Apache Spark Exam Dumps \u00a0and Practice Test Dumps   Question 81. Which DataFrame method is used to inspect the schema of a DataFrame in a tree-like format? printSchema() 2. describe() 3. explain() 4. summary() Correct Answer: 1. printSchema() Explanation: printSchema() displays the structure of a DataFrame, including column […]<\/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\/17811"}],"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=17811"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17811\/revisions"}],"predecessor-version":[{"id":17812,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17811\/revisions\/17812"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=17811"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=17811"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=17811"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}