{"id":17806,"date":"2026-09-21T11:34:29","date_gmt":"2026-09-21T11:34:29","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=17806"},"modified":"2026-09-21T11:34:29","modified_gmt":"2026-09-21T11:34:29","slug":"databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part3-q41-60","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-associate-developer-for-apache-spark-practice-test-questions-and-exam-dumps-part3-q41-60\/","title":{"rendered":"Databricks Certified Associate Developer for Apache Spark Practice Test Questions and Exam Dumps Part3 Q41-60"},"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 41.<\/b><\/p>\n

Which DataFrame method is commonly used to remove rows containing null values?<\/b><\/p>\n

    \n
  1. <\/b> na.drop()<\/span>
    \n<\/span>2.<\/b> repartition()<\/span>
    \n<\/span>3.<\/b> explain()<\/span>
    \n<\/span>4.<\/b> union()<\/span><\/li>\n<\/ol>\n

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

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

    na.drop() removes rows containing null values according to the specified options. It can be configured to consider all columns, selected columns, or minimum non-null thresholds. repartition() changes data partitioning, explain() displays execution plans, and union() combines compatible DataFrames. Null handling is an important part of Spark data-cleaning workflows because missing values can affect joins, aggregations, calculations, and downstream analytics.<\/span><\/p>\n

    Question 42.<\/b><\/p>\n

    Which method can replace null values in selected DataFrame columns with specified values?<\/b><\/p>\n

      \n
    1. <\/b> dropDuplicates()<\/span>
      \n<\/span>2.<\/b> na.fill()<\/span>
      \n<\/span>3.<\/b> cache()<\/span>
      \n<\/span>4.<\/b> orderBy()<\/span><\/li>\n<\/ol>\n

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

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

      na.fill() replaces null values with specified defaults. It can apply one value to compatible columns or use a mapping of column names to replacement values. dropDuplicates() removes duplicate rows, cache() stores computed data for reuse, and orderBy() sorts rows. Filling nulls is useful when downstream logic requires non-null values or when a meaningful default can be assigned without distorting the data.<\/span><\/p>\n

      Question 43.<\/b><\/p>\n

      Which DataFrame operation keeps only rows that satisfy a Boolean condition?<\/b><\/p>\n

        \n
      1. <\/b> alias()<\/span>
        \n<\/span>2.<\/b> persist()<\/span>
        \n<\/span>3.<\/b> filter()<\/span>
        \n<\/span>4.<\/b> describe()<\/span><\/li>\n<\/ol>\n

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

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

        filter() returns a new DataFrame containing only rows that satisfy the specified Boolean condition. It is equivalent in purpose to where() and is commonly used to narrow datasets before joins, aggregations, or writes. alias() assigns an alternate name, persist() stores computed data, and describe() returns summary statistics. Filtering early can also improve performance when Spark can push predicates closer to the underlying data source.<\/span><\/p>\n

        Question 44.<\/b><\/p>\n

        Which function returns the largest value in a numeric column during aggregation?<\/b><\/p>\n

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

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

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

          max() returns the largest value found in the specified column or expression. It is commonly used with groupBy() or aggregate operations to identify maximum values within categories or across an entire DataFrame. avg() calculates the mean, count() counts rows or non-null values depending on usage, and min() returns the smallest value. max() is therefore the correct aggregation when the highest value is required.<\/span><\/p>\n

          Question 45.<\/b><\/p>\n

          Which DataFrame method is commonly used to create groups before performing aggregations?<\/b><\/p>\n

            \n
          1. <\/b> groupBy()<\/span>
            \n<\/span>2.<\/b> collect()<\/span>
            \n<\/span>3.<\/b> limit()<\/span>
            \n<\/span>4.<\/b> explain()<\/span><\/li>\n<\/ol>\n

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

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

            groupBy() groups DataFrame rows according to one or more key columns so that aggregation functions can be applied to each group. Common aggregations include count(), sum(), avg(), min(), and max(). collect() returns results to the driver, limit() restricts the number of rows, and explain() displays execution plans. groupBy() often requires data redistribution because records sharing the same keys need to be processed together.<\/span><\/p>\n

            Question 46.<\/b><\/p>\n

            Which join type returns every row from both DataFrames, including unmatched rows from either side?<\/b><\/p>\n

              \n
            1. <\/b> Inner join<\/span>
              \n<\/span>2.<\/b> Full outer join<\/span>
              \n<\/span>3.<\/b> Left semi join<\/span>
              \n<\/span>4.<\/b> Left anti join<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2. Full outer join<\/b><\/p>\n

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

              A full outer join keeps matching rows as well as unmatched rows from both input DataFrames. When a row has no match on the opposite side, columns from that side are populated with null values. An inner join keeps only matches, a left semi join returns matching left-side rows, and a left anti join returns unmatched left-side rows. Full outer joins are useful when complete coverage of both datasets is required.<\/span><\/p>\n

              Question 47.<\/b><\/p>\n

              Which function can be used to split a string column into an array based on a delimiter or regular expression?<\/b><\/p>\n

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

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

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

                split() divides a string according to a delimiter or regular-expression pattern and returns the resulting pieces as an array. This is useful for parsing delimited text or extracting components from structured strings. concat() combines expressions, explode() expands array elements into rows, and struct() creates a nested struct. split() is therefore the appropriate function when a single string needs to be converted into multiple array elements.<\/span><\/p>\n

                Question 48.<\/b><\/p>\n

                Which method limits a DataFrame to at most a specified number of rows?<\/b><\/p>\n

                  \n
                1. <\/b> persist()<\/span>
                  \n<\/span>2.<\/b> coalesce()<\/span>
                  \n<\/span>3.<\/b> cache()<\/span>
                  \n<\/span>4.<\/b> limit()<\/span><\/li>\n<\/ol>\n

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

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

                  limit() returns a new DataFrame containing no more than the specified number of rows. It is useful for sampling output during development or restricting downstream processing when only a small subset is needed. persist() and cache() store computed data, while coalesce() changes the partition count. limit() is a transformation-like logical operation whose result is evaluated when an action is triggered.<\/span><\/p>\n

                  Question 49.<\/b><\/p>\n

                  Which Spark SQL function can convert all characters in a string column to lowercase?<\/b><\/p>\n

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

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

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

                    lower() converts alphabetic characters in a string expression to lowercase. It is commonly used for normalization before comparisons, joins, or deduplication when case differences should not matter. trim() removes leading and trailing whitespace, substring() extracts part of a string, and length() returns the number of characters. Normalizing text with lower() can help reduce mismatches caused by inconsistent capitalization.<\/span><\/p>\n

                    Question 50.<\/b><\/p>\n

                    Which function removes leading and trailing spaces from a string column?<\/b><\/p>\n

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

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

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

                      trim() removes leading and trailing whitespace from string values. This is useful during data cleaning because extra spaces can interfere with comparisons, joins, and deduplication. upper() changes letter case, regexp_replace() performs pattern-based replacement, and concat_ws() combines strings using a separator. Cleaning whitespace before downstream transformations can improve data consistency and reduce subtle matching errors.<\/span><\/p>\n

                      Question 51.<\/b><\/p>\n

                      Which function returns the length of a string column?<\/b><\/p>\n

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

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

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

                        length() returns the number of characters in a string expression. It is useful for validation, filtering, profiling, and data-quality checks. size() is commonly used for arrays or maps, count() is an aggregation, and rank() is a window function. length() provides a straightforward way to evaluate string size without collecting data to the driver.<\/span><\/p>\n

                        Question 52.<\/b><\/p>\n

                        Which operation is typically used to combine two DataFrames vertically by appending rows?<\/b><\/p>\n

                          \n
                        1. <\/b> join()<\/span>
                          \n<\/span>2.<\/b> groupBy()<\/span>
                          \n<\/span>3.<\/b> crossJoin()<\/span>
                          \n<\/span>4.<\/b> union()<\/span><\/li>\n<\/ol>\n

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

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

                          union() combines the rows of two compatible DataFrames, effectively appending one dataset beneath the other. The operation does not automatically remove duplicate rows. join() combines columns based on matching conditions, groupBy() creates groups for aggregation, and crossJoin() produces a Cartesian product. union() is therefore appropriate when datasets with compatible schemas need to be stacked vertically.<\/span><\/p>\n

                          Question 53.<\/b><\/p>\n

                          Which DataFrame method can be used to return only unique rows?<\/b><\/p>\n

                            \n
                          1. <\/b> distinct()<\/span>
                            \n<\/span>2.<\/b> cache()<\/span>
                            \n<\/span>3.<\/b> alias()<\/span>
                            \n<\/span>4.<\/b> explain()<\/span><\/li>\n<\/ol>\n

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

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

                            distinct() returns a DataFrame containing unique rows by removing exact duplicate records. It is similar to SQL SELECT DISTINCT and may require a shuffle so Spark can compare records across partitions. cache() stores data for reuse, alias() assigns an alternate name, and explain() displays query plans. distinct() is useful when all columns should be considered when determining duplicate rows.<\/span><\/p>\n

                            Question 54.<\/b><\/p>\n

                            Which DataFrame function can create a new column name for an expression inside select()?<\/b><\/p>\n

                              \n
                            1. <\/b> repartition()<\/span>
                              \n<\/span>2.<\/b> alias()<\/span>
                              \n<\/span>3.<\/b> checkpoint()<\/span>
                              \n<\/span>4.<\/b> count()<\/span><\/li>\n<\/ol>\n

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

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

                              alias() assigns a name to a column expression, which is especially useful when calculations or functions would otherwise produce unclear generated names. For example, sum(“amount”).alias(“total_amount”) gives the aggregation a meaningful column name. repartition() changes partitions, checkpoint() truncates lineage, and count() performs aggregation. Clear aliases make schemas easier to understand and downstream transformations easier to maintain.<\/span><\/p>\n

                              Question 55.<\/b><\/p>\n

                              Which Spark SQL function can replace substrings that match a regular-expression pattern?<\/b><\/p>\n

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

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

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

                                regexp_replace() searches a string using a regular-expression pattern and replaces matching content with a specified value. It is useful for cleaning phone numbers, identifiers, punctuation, or inconsistent text formats. trim() removes surrounding whitespace, split() divides strings into arrays, and length() counts characters. Regular-expression replacement provides flexible text-cleaning capabilities directly within distributed Spark transformations.<\/span><\/p>\n

                                Question 56.<\/b><\/p>\n

                                Which function can be used to create a column containing a fixed constant such as “US” for every row?<\/b><\/p>\n

                                  \n
                                1. <\/b> explode()<\/span>
                                  \n<\/span>2.<\/b> array()<\/span>
                                  \n<\/span>3.<\/b> broadcast()<\/span>
                                  \n<\/span>4.<\/b> lit()<\/span><\/li>\n<\/ol>\n

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

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

                                  lit() creates a literal expression that can be used as a constant-valued column. For example, withColumn(“country”, lit(“US”)) gives every row the value “US” in the new country column. explode() expands arrays or maps, array() constructs array expressions, and broadcast() is used for join optimization. lit() is useful for adding labels, processing dates, flags, or other fixed metadata to DataFrames.<\/span><\/p>\n

                                  Question 57.<\/b><\/p>\n

                                  Which operation is most appropriate when you need to repartition a DataFrame by a specific key column?<\/b><\/p>\n

                                    \n
                                  1. <\/b> repartition(“key”)<\/span>
                                    \n<\/span>2.<\/b> collect()<\/span>
                                    \n<\/span>3.<\/b> describe()<\/span>
                                    \n<\/span>4.<\/b> limit()<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 1. repartition(“key”)<\/b><\/p>\n

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

                                    repartition(“key”) redistributes rows according to the specified key expression and generally causes a shuffle. This can be useful before certain joins, writes, or downstream operations that benefit from data being partitioned by a particular column. collect() returns data to the driver, describe() calculates summary statistics, and limit() restricts row count. Repartitioning should be used carefully because shuffles can be expensive.<\/span><\/p>\n

                                    Question 58.<\/b><\/p>\n

                                    Which function is commonly used to concatenate strings with a separator between values?<\/b><\/p>\n

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

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

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

                                      concat_ws() concatenates multiple string expressions using a specified separator. For example, it can combine first and last names with a space or join address components with commas. concat() combines strings without automatically inserting a delimiter, collect_set() aggregates unique values into an array, and split() divides a string into pieces. concat_ws() is therefore convenient when formatted text requires consistent separators.<\/span><\/p>\n

                                      Question 59.<\/b><\/p>\n

                                      Which DataFrameReader option is commonly used to indicate that the first row of a CSV file contains column names?<\/b><\/p>\n

                                        \n
                                      1. <\/b> inferSchema<\/span>
                                        \n<\/span>2.<\/b> delimiter<\/span>
                                        \n<\/span>3.<\/b> header<\/span>
                                        \n<\/span>4.<\/b> mode<\/span><\/li>\n<\/ol>\n

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

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

                                        The header option tells Spark whether the first row of a CSV file should be interpreted as column names rather than data. It is commonly set to true when reading CSV files that include headers. inferSchema controls type inference, delimiter specifies field separation, and mode controls how malformed records are handled. Correct CSV options are important for producing the intended schema and avoiding accidental treatment of header text as data.<\/span><\/p>\n

                                        Question 60.<\/b><\/p>\n

                                        Which Spark DataFrame method is used to display a small number of rows in a human-readable tabular format?<\/b><\/p>\n

                                          \n
                                        1. <\/b> collect()<\/span>
                                          \n<\/span>2.<\/b> take()<\/span>
                                          \n<\/span>3.<\/b> explain()<\/span>
                                          \n<\/span>4.<\/b> show()<\/span><\/li>\n<\/ol>\n

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

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

                                          show() displays DataFrame rows in a readable tabular format and is commonly used during interactive development and debugging. By default, it shows a limited number of rows and can be configured to control truncation. collect() returns all rows to the driver, take() returns a specified number of Row objects, and explain() displays execution plans. show() is generally convenient when a developer simply wants to inspect sample output.<\/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 41. Which DataFrame method is commonly used to remove rows containing null values? na.drop() 2. repartition() 3. explain() 4. union() Correct Answer: 1. na.drop() Explanation: na.drop() removes rows containing null values according to the specified options. It can […]<\/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\/17806"}],"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=17806"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17806\/revisions"}],"predecessor-version":[{"id":17807,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/17806\/revisions\/17807"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=17806"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=17806"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=17806"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}