{"id":17819,"date":"2026-09-21T11:38:18","date_gmt":"2026-09-21T11:38:18","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=17819"},"modified":"2026-09-21T11:38:18","modified_gmt":"2026-09-21T11:38:18","slug":"databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part9-q161-180","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part9-q161-180\/","title":{"rendered":"Databricks Certified Associate Developer for Apache Spark Practice Test Questions and Exam Dumps Part9 Q161-180"},"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 161.<\/b><\/p>\n

Which Spark SQL function can be used to return the minimum value from a column?<\/b><\/p>\n

    \n
  1. <\/b> max()<\/span>
    \n<\/span>2.<\/b> min()<\/span>
    \n<\/span>3.<\/b> avg()<\/span>
    \n<\/span>4.<\/b> sum()<\/span><\/li>\n<\/ol>\n

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

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

    min() returns the smallest value from a specified column or expression. It is commonly used in aggregations across an entire DataFrame or within groups created using groupBy(). max() returns the largest value, avg() calculates the mean, and sum() calculates the total. min() is therefore the correct function when the goal is to identify the lowest numeric, date, or otherwise orderable value in a dataset.<\/span><\/p>\n

    Question 162.<\/b><\/p>\n

    Which function returns the number of rows in a DataFrame when used as an action?<\/b><\/p>\n

      \n
    1. <\/b> count()<\/span>
      \n<\/span>2.<\/b> collect()<\/span>
      \n<\/span>3.<\/b> size()<\/span>
      \n<\/span>4.<\/b> length()<\/span><\/li>\n<\/ol>\n

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

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

      count() is an action that triggers Spark execution and returns the number of rows in the DataFrame. collect() returns all rows to the driver, size() is typically used with arrays or maps, and length() is commonly used with strings or binary values. Because count() requires evaluating the relevant lineage, it can be computationally expensive on large datasets if no cached or optimized result is available.<\/span><\/p>\n

      Question 163.<\/b><\/p>\n

      Which DataFrame operation returns a new DataFrame with rows sorted by specified columns?<\/b><\/p>\n

        \n
      1. <\/b> groupBy()<\/span>
        \n<\/span>2.<\/b> repartition()<\/span>
        \n<\/span>3.<\/b> orderBy()<\/span>
        \n<\/span>4.<\/b> distinct()<\/span><\/li>\n<\/ol>\n

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

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

        orderBy() sorts rows according to one or more specified columns or expressions. It can sort values in ascending or descending order and may require a global shuffle to establish the requested ordering. groupBy() creates groups for aggregation, repartition() redistributes partitions, and distinct() removes duplicate rows. orderBy() is therefore appropriate when deterministic row ordering is required.<\/span><\/p>\n

        Question 164.<\/b><\/p>\n

        Which method can be used to sort rows within each partition without globally sorting the entire DataFrame?<\/b><\/p>\n

          \n
        1. <\/b> orderBy()<\/span>
          \n<\/span>2.<\/b> sort()<\/span>
          \n<\/span>3.<\/b> repartition()<\/span>
          \n<\/span>4.<\/b> sortWithinPartitions()<\/span><\/li>\n<\/ol>\n

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

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

          sortWithinPartitions() sorts records inside each existing partition without enforcing a global order across the entire DataFrame. This can be less expensive than a full orderBy() when only partition-local ordering is required. orderBy() and sort() generally imply global sorting semantics, while repartition() changes partition distribution rather than ordering records. sortWithinPartitions() can be useful before certain partitioned writes or partition-local processing.<\/span><\/p>\n

          Question 165.<\/b><\/p>\n

          Which Spark SQL function can return a random number for each row?<\/b><\/p>\n

            \n
          1. <\/b> rand()<\/span>
            \n<\/span>2.<\/b> lit()<\/span>
            \n<\/span>3.<\/b> monotonically_increasing_id()<\/span>
            \n<\/span>4.<\/b> hash()<\/span><\/li>\n<\/ol>\n

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

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

            rand() generates a pseudo-random value for each row, typically between 0 and 1. It can accept a seed to support reproducibility. lit() creates a constant expression, monotonically_increasing_id() creates increasing identifiers, and hash() calculates a hash value from expressions. rand() is useful for randomized ordering, test data generation, and probabilistic sampling logic.<\/span><\/p>\n

            Question 166.<\/b><\/p>\n

            Which function generates unique, monotonically increasing 64-bit integers across a DataFrame?<\/b><\/p>\n

              \n
            1. <\/b> row_number()<\/span>
              \n<\/span>2.<\/b> monotonically_increasing_id()<\/span>
              \n<\/span>3.<\/b> rank()<\/span>
              \n<\/span>4.<\/b> dense_rank()<\/span><\/li>\n<\/ol>\n

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

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

              monotonically_increasing_id() generates 64-bit integer identifiers that are unique and monotonically increasing, but they are not guaranteed to be consecutive. row_number() creates sequential values within a defined window, while rank() and dense_rank() provide ranking semantics. monotonically_increasing_id() is useful when a distributed unique identifier is needed without requiring a global consecutive numbering operation.<\/span><\/p>\n

              Question 167.<\/b><\/p>\n

              Which Spark SQL function can compute a hash value from one or more columns?<\/b><\/p>\n

                \n
              1. <\/b> crc32() only<\/span>
                \n<\/span>2.<\/b> sha2() only<\/span>
                \n<\/span>3.<\/b> hash()<\/span>
                \n<\/span>4.<\/b> md5() only<\/span><\/li>\n<\/ol>\n

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

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

                hash() computes a hash value across one or more input expressions. It can be useful for bucketing logic, comparisons, or deriving compact representations from multiple columns. Spark also provides cryptographic or checksum-oriented functions such as md5(), sha2(), and crc32(), but hash() is the general built-in function for computing a hash over arbitrary Spark expressions. The choice of hash function should depend on the intended use.<\/span><\/p>\n

                Question 168.<\/b><\/p>\n

                Which Spark SQL function can calculate an MD5 digest of a binary or string expression?<\/b><\/p>\n

                  \n
                1. <\/b> sha2()<\/span>
                  \n<\/span>2.<\/b> hash()<\/span>
                  \n<\/span>3.<\/b> crc32()<\/span>
                  \n<\/span>4.<\/b> md5()<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 4. md5()<\/b><\/p>\n

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

                  md5() calculates an MD5 hexadecimal digest for the supplied expression. It can be useful for checksums, comparisons, or generating deterministic fingerprints where cryptographic security is not required. sha2() supports SHA-2 digest variants, hash() returns Spark’s general hash representation, and crc32() calculates a CRC32 checksum. MD5 should not be used for modern cryptographic security purposes because of known collision weaknesses.<\/span><\/p>\n

                  Question 169.<\/b><\/p>\n

                  Which Spark SQL function can calculate a SHA-2 digest with a selected bit length?<\/b><\/p>\n

                    \n
                  1. <\/b> sha2()<\/span>
                    \n<\/span>2.<\/b> md5()<\/span>
                    \n<\/span>3.<\/b> hash()<\/span>
                    \n<\/span>4.<\/b> crc32()<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. sha2()<\/b><\/p>\n

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

                    sha2() computes a SHA-2 family digest for an input expression using a supported bit length such as 256 or 512. It is useful when a stronger cryptographic hash is needed than MD5. md5() computes an MD5 digest, hash() provides Spark’s general hash function, and crc32() returns a checksum. sha2() is therefore the appropriate function when SHA-2 hashing is required.<\/span><\/p>\n

                    Question 170.<\/b><\/p>\n

                    Which function can calculate a CRC32 checksum from a binary or string column?<\/b><\/p>\n

                      \n
                    1. <\/b> md5()<\/span>
                      \n<\/span>2.<\/b> crc32()<\/span>
                      \n<\/span>3.<\/b> sha2()<\/span>
                      \n<\/span>4.<\/b> hash()<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2. crc32()<\/b><\/p>\n

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

                      crc32() calculates a cyclic redundancy check value using the CRC32 algorithm. It is commonly used for integrity checks and checksums rather than cryptographic security. md5() and sha2() produce cryptographic-style digests, while hash() computes Spark’s general hash representation. crc32() is therefore appropriate when a lightweight checksum is needed for validation or comparison purposes.<\/span><\/p>\n

                      Question 171.<\/b><\/p>\n

                      Which Spark SQL function can replace all occurrences of a substring matching a regular expression?<\/b><\/p>\n

                        \n
                      1. <\/b> regexp_extract()<\/span>
                        \n<\/span>2.<\/b> split()<\/span>
                        \n<\/span>3.<\/b> regexp_replace()<\/span>
                        \n<\/span>4.<\/b> translate()<\/span><\/li>\n<\/ol>\n

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

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

                        regexp_replace() finds substrings matching a regular-expression pattern and replaces them with the specified replacement value. It is useful for data cleaning, removing punctuation, normalizing identifiers, or transforming free-form text. regexp_extract() extracts matching content, split() breaks strings into arrays, and translate() performs character-by-character substitution. regexp_replace() is therefore the best choice for regex-based replacement.<\/span><\/p>\n

                        Question 172.<\/b><\/p>\n

                        Which Spark SQL function extracts a substring that matches a regular-expression capture group?<\/b><\/p>\n

                          \n
                        1. <\/b> substring()<\/span>
                          \n<\/span>2.<\/b> split()<\/span>
                          \n<\/span>3.<\/b> regexp_replace()<\/span>
                          \n<\/span>4.<\/b> regexp_extract()<\/span><\/li>\n<\/ol>\n

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

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

                          regexp_extract() applies a regular expression to a string and returns the content matched by a specified capture group. It is useful for extracting IDs, codes, dates, or other patterned values from unstructured strings. substring() extracts content by position, split() divides strings by a pattern, and regexp_replace() performs substitutions. regexp_extract() is therefore the correct choice for retrieving regex-matched content.<\/span><\/p>\n

                          Question 173.<\/b><\/p>\n

                          Which Spark SQL function extracts a substring based on position and length?<\/b><\/p>\n

                            \n
                          1. <\/b> substring()<\/span>
                            \n<\/span>2.<\/b> split()<\/span>
                            \n<\/span>3.<\/b> regexp_extract()<\/span>
                            \n<\/span>4.<\/b> concat()<\/span><\/li>\n<\/ol>\n

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

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

                            substring() extracts a specified portion of a string based on a starting position and length. It is useful when fields contain fixed-position codes or when only a known segment is required. split() divides a string into multiple elements, regexp_extract() uses pattern matching, and concat() combines strings. substring() is therefore appropriate for position-based string extraction.<\/span><\/p>\n

                            Question 174.<\/b><\/p>\n

                            Which function can concatenate multiple strings using a specified separator?<\/b><\/p>\n

                              \n
                            1. <\/b> concat()<\/span>
                              \n<\/span>2.<\/b> concat_ws()<\/span>
                              \n<\/span>3.<\/b> collect_list()<\/span>
                              \n<\/span>4.<\/b> format_string()<\/span><\/li>\n<\/ol>\n

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

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

                              concat_ws() concatenates multiple string expressions while inserting a specified separator between them. It is useful for assembling formatted names, addresses, or delimited output fields. concat() combines expressions directly without automatically inserting a separator, collect_list() creates an array from grouped values, and format_string() uses formatting patterns. concat_ws() is convenient when consistent delimiters are required.<\/span><\/p>\n

                              Question 175.<\/b><\/p>\n

                              Which Spark SQL function can pad the left side of a string to a specified length?<\/b><\/p>\n

                                \n
                              1. <\/b> trim()<\/span>
                                \n<\/span>2.<\/b> substring()<\/span>
                                \n<\/span>3.<\/b> lpad()<\/span>
                                \n<\/span>4.<\/b> rpad()<\/span><\/li>\n<\/ol>\n

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

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

                                lpad() adds padding characters to the left side of a string until the requested total length is reached. It is commonly used to normalize codes, account numbers, or numeric identifiers represented as text. rpad() pads the right side, trim() removes whitespace, and substring() extracts portions of strings. lpad() is therefore the appropriate function for left-side padding.<\/span><\/p>\n

                                Question 176.<\/b><\/p>\n

                                Which function pads the right side of a string to a specified length?<\/b><\/p>\n

                                  \n
                                1. <\/b> lpad()<\/span>
                                  \n<\/span>2.<\/b> trim()<\/span>
                                  \n<\/span>3.<\/b> concat()<\/span>
                                  \n<\/span>4.<\/b> rpad()<\/span><\/li>\n<\/ol>\n

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

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

                                  rpad() adds specified characters to the right side of a string until it reaches the desired length. lpad() performs the same type of operation on the left side. trim() removes leading and trailing whitespace, while concat() combines strings. rpad() is useful when fixed-width output or standardized text lengths are required.<\/span><\/p>\n

                                  Question 177.<\/b><\/p>\n

                                  Which Spark SQL function removes whitespace from both the beginning and end of a string?<\/b><\/p>\n

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

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

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

                                    trim() removes whitespace from both ends of a string. ltrim() removes leading whitespace only, while rtrim() removes trailing whitespace only. regexp_replace() can perform more flexible pattern-based cleaning but is unnecessary for simple surrounding whitespace. trim() is commonly used before joins, comparisons, and deduplication to avoid mismatches caused by accidental spaces.<\/span><\/p>\n

                                    Question 178.<\/b><\/p>\n

                                    Which Spark SQL function removes whitespace only from the left side of a string?<\/b><\/p>\n

                                      \n
                                    1. <\/b> trim()<\/span>
                                      \n<\/span>2.<\/b> ltrim()<\/span>
                                      \n<\/span>3.<\/b> rtrim()<\/span>
                                      \n<\/span>4.<\/b> lower()<\/span><\/li>\n<\/ol>\n

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

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

                                      ltrim() removes leading whitespace from the beginning of a string while leaving trailing whitespace unchanged. trim() removes whitespace from both sides, rtrim() removes only trailing whitespace, and lower() changes alphabetic characters to lowercase. ltrim() is useful when only leading padding or accidental spaces should be removed.<\/span><\/p>\n

                                      Question 179.<\/b><\/p>\n

                                      Which Spark SQL function removes whitespace only from the right side of a string?<\/b><\/p>\n

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

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

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

                                        rtrim() removes trailing whitespace from the right side of a string while preserving leading whitespace. trim() removes whitespace from both ends, ltrim() removes leading whitespace, and upper() converts text to uppercase. rtrim() is useful when records contain unwanted trailing spaces that interfere with comparisons or produce inconsistent output.<\/span><\/p>\n

                                        Question 180.<\/b><\/p>\n

                                        Which Spark SQL function converts the first letter of each word in a string to uppercase?<\/b><\/p>\n

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

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

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

                                          initcap() converts the first character of each word to uppercase and the remaining characters according to title-case behavior. It is useful for formatting names, labels, and display-oriented text. upper() converts all letters to uppercase, lower() converts all letters to lowercase, and capitalize() is not the standard Spark SQL function for this behavior. initcap() is therefore the appropriate choice for word-by-word capitalization.<\/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 161. Which Spark SQL function can be used to return the minimum value from a column? max() 2. min() 3. avg() 4. sum() Correct Answer: 2. min() Explanation: min() returns the smallest value from a specified column or […]<\/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\/17819"}],"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=17819"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17819\/revisions"}],"predecessor-version":[{"id":17820,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17819\/revisions\/17820"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=17819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=17819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=17819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}