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Spark SQL MCQs and Answers With Explanation: Spark SQL is a widely used tool for processing structured and semi-structured data in a distributed computing environment. It provides a powerful SQL interface for processing data using Apache Spark. To help you test your knowledge of Spark SQL, we have compiled a set of top 65 Spark SQL MCQs and Answers with explanations.
Spark SQL MCQs and Answers
This Spark SQL quiz covers multiple topics, including basic concepts, syntax, data manipulation, and optimization techniques. Whether you are a beginner or an experienced data analyst, this Spark SQL Multiple Choice Questions and Answers quiz can help you enhance your knowledge and prepare for Spark SQL-related interviews and certifications.
Spark SQL Multiple Choice Questions and Answers
| Name | Spark SQL |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 65 Spark SQL MCQs
1. Which of the following is NOT a feature of Spark SQL?
A. High-level APIs for structured data
B. Integration with Hadoop Distributed File System (HDFS)
C. Support for real-time streaming
D. In-memory computation engine
Answer: C
Explanation: Spark SQL is a module in Apache Spark that provides a programming interface for working with structured data using SQL, DataFrames, and Datasets. It supports integration with HDFS and other data sources, and it has an in-memory computation engine that provides fast performance for data processing. However, Spark SQL is not designed for real-time streaming.
2. Which of the following statements is true about DataFrames in Spark SQL?
A. DataFrames are immutable.
B. DataFrames are a collection of RDDs.
C. DataFrames are optimized for distributed computation.
D. DataFrames can be created from CSV files, JSON files, and Hive tables.
Answer: D
Explanation: DataFrames are a distributed collection of data organized into named columns. They are designed to be similar to tables in a relational database, and they can be created from various data sources, including CSV files, JSON files, and Hive tables. DataFrames are not immutable, and they are not a collection of RDDs. However, they are optimized for distributed computation, which makes them an efficient way to work with large datasets.
3. Which of the following is NOT a benefit of using Spark SQL?
A. Easy integration with Hadoop and other data sources
B. High-level APIs for structured data processing
C. Efficient support for real-time streaming
D. In-memory computation engine for fast processing
Answer: C
Explanation: Spark SQL does not have efficient support for real-time streaming, as it is not designed for this purpose. However, it does provide other benefits, such as easy integration with Hadoop and other data sources, high-level APIs for structured data processing, and an in-memory computation engine for fast processing.
4. Which of the following is a valid way to create a DataFrame in Spark SQL?
A. val df = sqlContext.read.text(“data.txt”)
B. val df = sqlContext.createDataFrame(Seq((“Alice”, 25), (“Bob”, 30)))
C. val df = sqlContext.load(“data.parquet”)
D. val df = sqlContext.read.csv(“data.csv”)
Answer: B
Explanation: Option B is a valid way to create a DataFrame in Spark SQL. It creates a DataFrame from a sequence of tuples, where each tuple represents a row of data. Option A reads a text file and creates a DataFrame with a single column containing the text data. Option C loads a Parquet file, which is a columnar storage format used by Spark SQL. Option D reads a CSV file and creates a DataFrame with columns based on the header row of the CSV file.
5. Which of the following operations is NOT supported by Spark SQL?
A. Joins
B. Grouping and aggregation
C. Sorting
D. Machine learning algorithms
Answer: D
Explanation: Spark SQL provides support for joins, grouping and aggregation, and sorting, but it does not provide built-in machine learning algorithms. However, Spark MLlib is a separate module in Apache Spark that provides machine learning algorithms and utilities.
6. Which of the following is a valid way to register a DataFrame as a temporary table in Spark SQL?
A. df.registerTempTable(“tempTable”)
B. sqlContext.registerTempTable(“tempTable”, df)
C. registerTempTable(df, “tempTable”)
D. tempTable.register(df)
Answer: A
Explanation: Option A is the correct way to register a DataFrame as a temporary table in Spark SQL. It allows the DataFrame to be queried using SQL syntax. Option B is not valid,
as it passes the DataFrame as a second argument to the registerTempTable method, which does not accept a DataFrame as an argument. Option C is not a valid Spark SQL method for registering a temporary table. Option D is also not valid, as it attempts to register the DataFrame as a property of the temporary table, rather than the other way around.
7. Which of the following statements is true about Spark SQL’s Catalyst optimizer?
A. It is a rule-based optimizer.
B. It is a cost-based optimizer.
C. It can only optimize SQL queries.
D. It does not support predicate pushdown.
Answer: B
Explanation: Spark SQL’s Catalyst optimizer is a cost-based optimizer, which means that it takes into account the cost of executing different parts of a query when optimizing the query plan. It is not a rule-based optimizer, as it does not rely on fixed rules for optimization. Catalyst can optimize both SQL queries and DataFrame operations. It supports predicate pushdown, which means that it can push filters down to the data sources to reduce the amount of data that needs to be read.
8. Which of the following Spark SQL functions can be used to round the values in a column to a specific number of decimal places in a DataFrame?
A. round
B. floor
C. ceil
D. All of the above
Answer: A
Explanation: The round function can be used to round the values in a column to a specific number of decimal places in a DataFrame. Option B (floor) is used to round down the values in a column to the nearest integer, and option C (ceil) is used to round up the values in a column to the nearest integer.
9. Which of the following is a valid way to execute a SQL query on a DataFrame in Spark SQL?
A. df.select(“name”, “age”).where(“age > 25”).execute()
B. sqlContext.executeSql(“SELECT name, age FROM people WHERE age > 25”)
C. sqlContext.sql(“SELECT name, age FROM people WHERE age > 25”)
D. df.executeSql(“SELECT name, age FROM people WHERE age > 25”)
Answer: C
Explanation: Option C is the correct way to execute a SQL query on a DataFrame in Spark SQL. It uses the sql method of the SQLContext object to execute the query. Option A is not valid, as it attempts to call an execute method on a DataFrame, which does not exist. Option B is not a valid method for executing SQL queries in Spark SQL. Option D is also not valid, as it attempts to call the executeSql method on a DataFrame, which does not exist.
10. Which of the following is NOT a built-in function in Spark SQL?
A. concat_ws
B. split
C. array
D. reduceByKey
Answer: D
Explanation: reduceByKey is not a built-in function in Spark SQL, but it is a method in the Spark API for working with RDDs. The other options are all built-in functions in Spark SQL. The concat_ws function concatenates a set of strings using a delimiter. The split function splits a string into an array of substrings based on a delimiter. The array function creates an array from a set of input values.
11. Which of the following is a valid way to write a DataFrame to a CSV file in Spark SQL?
A. df.saveAsTable(“output.csv”)
B. df.write.csv(“output.csv”)
C. df.save(“output.csv”, “csv”)
D. sqlContext.write.csv(“output.csv”, df)
Answer: B
Explanation: Option B is the correct way to write a DataFrame to a CSV file in Spark SQL. It uses the write method of the DataFrame object to write the data in CSV format. Option A is not valid, as saveAsTable is used to save a DataFrame as a table in a data source, not to write it to a file. Option C is not valid, as save is not a method of the DataFrame object. Option D is also not valid, as write.csv is a method of the DataFrameWriter object, which is not available in the sqlContext object.
12. Which of the following is a valid way to specify a schema for a DataFrame in Spark SQL?
A. val schema = “name STRING, age INT”; val df = sqlContext.read.format(“csv”).schema(schema).load(“people.csv”)
B. val schema = StructType(Seq(StructField(“name”, StringType), StructField(“age”, IntegerType))); val df = sqlContext.read.format(“csv”).schema(schema).load(“people.csv”)
C. val schema = StructField(“name”, StringType) :: StructField(“age”, IntegerType) :: Nil; val df = sqlContext.read.format(“csv”).schema(schema).load(“people.csv”)
D. val schema = Map(“name” -> “STRING”, “age” -> “INT”); val df = sqlContext.read.format(“csv”).schema(schema).load(“people.csv”)
Answer: B
Explanation: Option B is the correct way to specify a schema for a DataFrame in Spark SQL. It uses the StructType class to define the schema as a sequence of StructField objects, which specify the name and data type of each column in the DataFrame. Option A is not valid, as it uses a string to define the schema, which is not a supported method. Option C is also not valid, as it uses a list of StructField objects, which is not a supported method. Option D is not valid, as it uses a map to define the schema, which is not a supported method.
13. Which of the following is NOT a valid format for reading data into a DataFrame in Spark SQL?
A. CSV
B. JSON
C. AVRO
D. HTML
Answer: D
Explanation: HTML is not a valid format for reading data into a DataFrame in Spark SQL. The other options are all valid formats. CSV (Comma-Separated Values) is a common format for storing tabular data. JSON (JavaScript Object Notation) is a lightweight data interchange format. AVRO is a row-oriented data serialization format.
14. Which of the following is a valid way to perform a join operation between two DataFrames in Spark SQL?
A. val joined = df1.join(df2, “id”)
B. val joined = df1.join(df2, $”id” === $”id”)
C. val joined = df1.crossJoin(df2)
D. val joined = df1.union(df2)
Answer: A
Explanation: Option A is the correct way to perform a join operation between two DataFrames in Spark SQL. It uses the join method of the first DataFrame to join it with the second DataFrame, using the specified join key (“id” in this case). Option B is also valid, but it uses Spark’s SQL-style syntax for specifying the join condition. Option C performs a cross join, which produces a Cartesian product of the two DataFrames. Option D performs a union operation, which combines the rows of the two DataFrames into a single DataFrame.
15. Which of the following is a valid way to perform an aggregation operation on a DataFrame in Spark SQL?
A. df.groupBy(“id”).sum()
B. df.select(“id”).distinct()
C. df.orderBy(“id”)
D. df.filter($”id” > 5)
Answer: A
Explanation: Option A is the correct way to perform an aggregation operation on a DataFrame in Spark SQL. It uses the groupBy method of the DataFrame to group the data by the specified column (“id” in this case), and then applies an aggregation function (sum in this case) to each group. Option B selects distinct values from the “id” column. Option C orders the DataFrame by the “id” column. Option D filters the DataFrame to include only rows where the “id” column is greater than
16. Which of the following functions can be used to aggregate data in Spark SQL?
A. sum
B. avg
C. max
D. All of the above
Answer: D
Explanation: All of the above functions (sum, avg, and max) can be used to aggregate data in Spark SQL. These functions are just a few examples of the many available aggregation functions in Spark SQL.
17. Which of the following functions can be used to transform data in Spark SQL?
A. split
B. substring
C. trim
D. All of the above
Answer: D
Explanation: All of the above functions (split, substring, and trim) can be used to transform data in Spark SQL. These functions are just a few examples of the many available transformation functions in Spark SQL.
18. Which of the following statements is true regarding the difference between a DataFrame and a Dataset in Spark SQL?
A. A DataFrame is a type of Dataset.
B. A Dataset is a type of DataFrame.
C. DataFrames are immutable, while Datasets are mutable.
D. Datasets have a more complex API than DataFrames.
Answer: A
Explanation: A DataFrame is a type of Dataset in Spark SQL. In fact, the DataFrame API is just a type-safe wrapper around the more general Dataset API. Option B is incorrect, as a Dataset is not a type of DataFrame. Option C is incorrect, as both DataFrames and Datasets are immutable in Spark SQL. Option D is also incorrect, as the Dataset API is not more complex than the DataFrame API, but rather more general and flexible.
19. Which of the following is a valid way to filter a DataFrame in Spark SQL?
A. val filtered = df.filter(“age > 30″)
B. val filtered = df.filter($”age” > 30)
C. val filtered = df.where(“age > 30”)
D. All of the above
Answer: D
Explanation: All of the above options are valid ways to filter a DataFrame in Spark SQL. They use different syntax for specifying the filter condition, but all achieve the same result.
20. Which of the following is a valid way to select specific columns from a DataFrame in Spark SQL?
A. val selected = df.select(“name”, “age”)
B. val selected = df.selectExpr(“name”, “age”)
C. val selected = df.withColumn(“name”, $”name”).withColumn(“age”, $”age”)
D. All of the above
Answer: D
Explanation: All of the above options are valid ways to select specific columns from a DataFrame in Spark SQL. They use different syntax for specifying the columns to select, but all achieve the same result.
21. Which of the following statements is true regarding the “explain” method in Spark SQL?
A. It displays the execution plan for a DataFrame operation.
B. It displays the schema of a DataFrame.
C. It displays the first few rows of a DataFrame.
D. It displays the metadata of a DataFrame.
Answer: A
Explanation: The “explain” method in Spark SQL displays the execution plan for a DataFrame operation. This can be useful for understanding how Spark will execute the operation and for identifying potential performance bottlenecks. Options B, C, and D are all incorrect, as they describe functions that are not performed by the “explain” method.
22. Which of the following functions can be used to join two DataFrames in Spark SQL?
A. join
B. crossJoin
C. union
D. All of the above
Answer: A
Explanation: The join function can be used to join two DataFrames in Spark SQL. Option B (crossJoin) performs a cross-join operation, which is a special type of join where all combinations of rows from the two DataFrames are produced. Option C (union) is used to combine two DataFrames vertically, but not to join them horizontally.
23. Which of the following is a valid way to write a DataFrame to a Parquet file in Spark SQL?
A. df.write.csv(“path/to/csv/file”)
B. df.write.parquet(“path/to/parquet/file”)
C. df.write.json(“path/to/json/file”)
D. All of the above
Answer: B
Explanation: The write.parquet method can be used to write a DataFrame to a Parquet file in Spark SQL. Options A and C write the DataFrame to a CSV or JSON file, respectively.
24. Which of the following is a valid way to cache a DataFrame in Spark SQL?
A. df.cache()
B. df.persist()
C. df.memory()
D. All of the above
Answer: B
Explanation: The persist method can be used to cache a DataFrame in Spark SQL. Option A (cache) is a shortcut for calling persist with the default storage level, and option C (memory) is not a valid method in Spark SQL.
25. Which of the following storage levels can be used to cache a DataFrame in memory only in Spark SQL?
A. MEMORY_ONLY
B. MEMORY_ONLY_SER
C. MEMORY_AND_DISK
D. DISK_ONLY
Answer: A
Explanation: The MEMORY_ONLY storage level can be used to cache a DataFrame in memory only in Spark SQL. Option B (MEMORY_ONLY_SER) stores the data in a serialized format to save memory, option C (MEMORY_AND_DISK) spills data to disk if it doesn’t fit in memory, and option D (DISK_ONLY) stores the data only on disk.
26. Which of the following statements is true about the partitioning of a DataFrame in Spark SQL?
A. A DataFrame can have only one partition.
B. The number of partitions of a DataFrame is determined automatically by Spark SQL.
C. The number of partitions of a DataFrame can be set manually using the repartition method.
D. The partitioning of a DataFrame cannot be changed once it has been created.
Answer: C
Explanation: The number of partitions of a DataFrame can be set manually using the repartition method in Spark SQL. Option A is false as a DataFrame can have multiple partitions. Option B is not always true, as the number of partitions can depend on the source of the data. Option D is false as the partitioning of a DataFrame can be changed using methods like repartition or coalesce.
27. Which of the following functions can be used to group a DataFrame by one or more columns in Spark SQL?
A. groupBy
B. agg
C. select
D. All of the above
Answer: A
Explanation: The groupBy function can be used to group a DataFrame by one or more columns in Spark SQL. Option B (agg) can be used to compute aggregate functions like sum or count on grouped data, and option C (select) is used to select columns from a DataFrame.
28. Which of the following functions can be used to sort a DataFrame by one or more columns in Spark SQL?
A. sort
B. orderBy
C. sortWithinPartitions
D. All of the above
Answer: B
Explanation: The orderBy function can be used to sort a DataFrame by one or more columns in Spark SQL. Option A (sort) is a shortcut for calling orderBy with ascending order, and option C (sortWithinPartitions) sorts the data within each partition, but not across partitions.
29. Which of the following aggregate functions can be used to compute the average of a numeric column in Spark SQL?
A. sum
B. count
C. avg
D. All of the above
Answer: C
Explanation: The avg aggregate function can be used to compute the average of a numeric column in Spark SQL. Option A (sum) computes the sum of a numeric column, and option B (count) computes the number of non-null values in a column.
30. Which of the following aggregate functions can be used to compute the maximum value of a column in Spark SQL?
A. max
B. min
C. count
D. All of the above
Answer: A
Explanation: The max aggregate function can be used to compute the maximum value of a column in Spark SQL. Option B (min) computes the minimum value of a column, and option C (count) computes the number of non-null values in a column.
31. Which of the following statements is true about caching a DataFrame in Spark SQL?
A. Caching a DataFrame always speeds up subsequent operations on the same DataFrame.
B. Caching a DataFrame can result in memory usage issues if the DataFrame is too large to fit in memory.
C. Caching a DataFrame makes it immutable, and changes to the original DataFrame are not reflected in the cached copy.
D. Caching a DataFrame is the same as persisting it to disk.
Answer: B
Explanation: Caching a DataFrame can speed up subsequent operations on the same DataFrame if the DataFrame is accessed multiple times, but it can result in memory usage issues if the DataFrame is too large to fit in memory. Option C is false, as caching a DataFrame does not make it immutable, and changes to the original DataFrame are reflected in the cached copy. Option D is false, as caching a DataFrame only stores it in memory, not on disk.
32. Which of the following is true about Spark SQL’s DataFrameWriter?
A. It is used to read data from external sources into a DataFrame.
B. It is used to write data from a DataFrame to external sources.
C. It is used to execute SQL queries on a DataFrame.
D. It is used to define the schema of a DataFrame.
Answer: B
Explanation: Spark SQL’s DataFrameWriter is used to write data from a DataFrame to external sources, like a database or a file. Option A is false, as reading data into a DataFrame is done using the DataFrameReader. Option C is false, as executing SQL queries on a DataFrame is done using Spark SQL’s SQLContext. Option D is false, as defining the schema of a DataFrame is done using the StructType object.
33. Which of the following file formats is not supported by Spark SQL’s DataFrame API?
A. CSV
B. JSON
C. Parquet
D. XML
Answer: D
Explanation: Spark SQL’s DataFrame API supports various file formats, like CSV, JSON, and Parquet, but it does not support XML.
34. Which of the following SQL clauses is used to filter rows based on a condition in Spark SQL?
A. GROUP BY
B. ORDER BY
C. WHERE
D. JOIN
Answer: C
Explanation: The WHERE clause is used to filter rows based on a condition in Spark SQL. Option A (GROUP BY) is used to group rows based on one or more columns, option B (ORDER BY) is used to sort rows based on one or more columns, and option D (JOIN) is used to combine data from multiple tables based on a join condition.
35. Which of the following functions is used to add a new column to a DataFrame in Spark SQL?
A. withColumn
B. select
C. where
D. orderBy
Answer: A
Explanation: The withColumn function is used to add a new column to a DataFrame in Spark SQL. Option B (select) is used to select columns from a DataFrame, option C (where) is used to filter rows based on a condition, and option D (orderBy) is used to sort rows based on one or more columns.
36. Which of the following Spark SQL functions can be used to convert a string to a date?
A. to_date
B. to_timestamp
C. date_format
D. All of the above
Answer: A
Explanation: The to_date function can be used to convert a string to a date in Spark SQL. Option B (to_timestamp) can be used to convert a string to a timestamp, and option C (date_format) can be used to format a date or timestamp as a string.
37. Which of the following functions is used to pivot a DataFrame in Spark SQL?
A. pivot
B. explode
C. split
D. groupBy
Answer: A
Explanation: The pivot function is used to pivot a DataFrame in Spark SQL, i.e., it transposes rows into columns. Option B (explode) is used to transform an array or a map column into multiple rows, option C (split) is used to split a string column into multiple columns based on a delimiter, and option D (groupBy) is used to group rows based on one or more columns.
38. Which of the following methods is used to register a DataFrame as a temporary table in Spark SQL?
A. createGlobalTempView
B. createOrReplaceTempView
C. createTempView
D. registerTempTable
Answer: B
Explanation: The createOrReplaceTempView method is used to register a DataFrame as a temporary table in Spark SQL. Option A (createGlobalTempView) is used to register a DataFrame as a global temporary view, option C (createTempView) is an alternative method to createOrReplaceTempView, and option D (registerTempTable) is a deprecated method.
39. Which of the following Spark SQL functions can be used to compute the rank of rows in a DataFrame?
A. rank
B. dense_rank
C. row_number
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., rank, dense_rank, and row_number, can be used to compute the rank of rows in a DataFrame. The rank function computes the rank of each distinct value in a partition, the dense_rank function computes the rank of each distinct value in a partition with no gaps, and the row_number function computes a unique monotonically increasing number for each row.
40. Which of the following Spark SQL functions can be used to compute the sum of a column in a DataFrame?
A. sum
B. avg
C. max
D. All of the above
Answer: A
Explanation: The sum function can be used to compute the sum of a column in a DataFrame. Option B (avg) can be used to compute the average of a column, option C (max) can be used to compute the maximum value of a column, and so on.
41. Which of the following is not a Spark SQL built-in data source?
A. CSV
B. Parquet
C. JSON
D. MySQL
Answer: D
Explanation: Spark SQL has built-in support for various data sources, like CSV, Parquet, and JSON, but MySQL is not a built-in data source. However, Spark SQL can read and write data from and to MySQL using JDBC.
42. Which of the following is not a Spark SQL window function?
A. lag
B. lead
C. rank
D. groupBy
Answer: D
Explanation: GroupBy is not a Spark SQL window function. It is used to group rows based on one or more columns. The lag function is used to access a previous row in a window, the lead function is used to access a next row in a window, and the rank function is used to compute the rank of rows in a window.
43. Which of the following Spark SQL functions can be used to compute the correlation between two columns in a DataFrame?
A. corr
B. covar_pop
C. covar_samp
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., corr, covar_pop, and covar_samp, can be used to compute the correlation between two columns in a DataFrame. The corr function computes the Pearson correlation coefficient between two columns, the covar_pop function computes the population covariance between two columns, and the covar_samp function computes the sample covariance between two columns.
44. Which of the following Spark SQL functions can be used to compute the percentile of a column in a DataFrame?
A. percentile
B. percentile_approx
C. percent_rank
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., percentile, percentile_approx, and percent_rank, can be used to compute the percentile of a column in a DataFrame. The percentile function computes the exact percentile, the percentile_approx function computes an approximate percentile, and the percent_rank function computes the percentage rank of each row.
45. Which of the following Spark SQL functions can be used to extract the year from a date column in a DataFrame?
A. year
B. month
C. day
D. All of the above
Answer: A
Explanation: The year function can be used to extract the year from a date column in a DataFrame. Option B (month) can be used to extract the month, option C (day) can be used to extract the day, and so on.
46. Which of the following Spark SQL functions can be used to convert a string column to a timestamp column in a DataFrame?
A. to_date
B. to_timestamp
C. to_unix_timestamp
D. All of the above
Answer: B
Explanation: The to_timestamp function can be used to convert a string column to a timestamp column in a DataFrame. Option A (to_date) can be used to convert a string column to a date column, option C (to_unix_timestamp) can be used to convert a string column to a UNIX timestamp, and so on.
47. Which of the following Spark SQL functions can be used to format a timestamp column in a DataFrame?
A. date_format
B. from_unixtime
C. unix_timestamp
D. All of the above
Answer: A
Explanation: The date_format function can be used to format a timestamp column in a DataFrame. Option B (from_unixtime) can be used to convert a UNIX timestamp to a timestamp column, option C (unix_timestamp) can be used to convert a string column to a UNIX timestamp, and so on.
48. Which of the following Spark SQL functions can be used to cast a column to a different data type in a DataFrame?
A. cast
B. convert
C. as
D. All of the above
Answer: A
Explanation: The cast function can be used to cast a column to a different data type in a DataFrame. Option B (convert) is not a Spark SQL function, and option C (as) is used as a shorthand for cast.
49. Which of the following Spark SQL functions can be used to remove duplicates from a DataFrame?
A. distinct
B. dropDuplicates
C. removeDuplicates
D. All of the above
Answer: B
Explanation: The dropDuplicates function can be used to remove duplicates from a DataFrame. Option A (distinct) is used to select distinct rows from a DataFrame, and option C (removeDuplicates) is not a valid Spark SQL function.
50. Which of the following Spark SQL functions can be used to filter rows from a DataFrame based on a condition?
A. filter
B. where
C. select
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., filter, where, and select, can be used to filter rows from a DataFrame based on a condition. The filter and where functions are synonyms for each other and are used to apply a boolean expression as a filter on the rows, and the select function is used to select specific columns from a DataFrame.
51. Which of the following Spark SQL functions can be used to group a DataFrame by one or more columns?
A. groupBy
B. agg
C. pivot
D. All of the above
Answer: A
Explanation: The groupBy function can be used to group a DataFrame by one or more columns. Option B (agg) is used to apply aggregation functions on a DataFrame, and option C (pivot) is used to pivot a DataFrame by grouping data based on two columns.
52. Which of the following Spark SQL functions can be used to join two DataFrames based on a common column?
A. join
B. union
C. intersect
D. All of the above
Answer: A
Explanation: The join function can be used to join two DataFrames based on a common column. Option B (union) is used to combine two DataFrames with the same schema, and option C (intersect) is used to get the intersection of two DataFrames.
53. Which of the following Spark SQL functions can be used to sort a DataFrame by one or more columns?
A. sort
B. orderBy
C. asc
D. All of the above
Answer: B
Explanation: The orderBy function can be used to sort a DataFrame by one or more columns. Option A (sort) is a synonym for orderBy, and option C (asc) is used to sort a column in ascending order.
54. Which of the following Spark SQL functions can be used to calculate the average of a column in a DataFrame?
A. avg
B. mean
C. sum
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., avg, mean, and sum, can be used to calculate the average of a column in a DataFrame. The avg and mean functions are synonyms for each other and are used to calculate the mean of a column, and the sum function is used to calculate the sum of a column.
55. Which of the following Spark SQL functions can be used to calculate the minimum value of a column in a DataFrame?
A. min
B. max
C. count
D. All of the above
Answer: A
Explanation: The min function can be used to calculate the minimum value of a column in a DataFrame. Option B (max) is used to calculate the maximum value of a column, and option C (count) is used to count the number of non-null values in a column.
56. Which of the following Spark SQL functions can be used to calculate the variance of a column in a DataFrame?
A. var
B. stddev
C. covar_pop
D. All of the above
Answer: A
Explanation: The var function can be used to calculate the variance of a column in a DataFrame. Option B (stddev) is used to calculate the standard deviation of a column, and option C (covar_pop) is used to calculate the population covariance between two columns.
57. Which of the following Spark SQL functions can be used to calculate the skewness of a column in a DataFrame?
A. skewness
B. kurtosis
C. covar_samp
D. All of the above
Answer: A
Explanation: The skewness function can be used to calculate the skewness of a column in a DataFrame. Option B (kurtosis) is used to calculate the kurtosis of a column, and option C (covar_samp) is used to calculate the sample covariance between two columns.
58. Which of the following Spark SQL functions can be used to calculate the correlation between two columns in a DataFrame?
A. corr
B. covar_pop
C. covar_samp
D. All of the above
Answer: D
Explanation: All of the above functions, i.e., corr, covar_pop, and covar_samp, can be used to calculate the correlation between two columns in a DataFrame. The corr function is used to calculate the Pearson correlation coefficient between two columns, while the covar_pop and covar_samp functions are used to calculate the population and sample covariance between two columns, respectively.
59. Which of the following Spark SQL functions can be used to calculate the percentile of a column in a DataFrame?
A. percentile
B. quantile
C. rank
D. All of the above
Answer: A
Explanation: The percentile function can be used to calculate the percentile of a column in a DataFrame. Option B (quantile) is a synonym for percentile, and option C (rank) is used to assign a rank to each row in a DataFrame based on a specific column.
60. Which of the following Spark SQL functions can be used to calculate the cumulative sum of a column in a DataFrame?
A. cumsum
B. sum
C. aggregate
D. All of the above
Answer: A
Explanation: The cumsum function can be used to calculate the cumulative sum of a column in a DataFrame. Option B (sum) is used to calculate the sum of a column, and option C (aggregate) is used to apply an aggregation function on a column or group of columns.
61. Which of the following Spark SQL functions can be used to create a new DataFrame by applying a UDF (user-defined function) to an existing DataFrame?
A. withColumn
B. select
C. filter
D. All of the above
Answer: A
Explanation: The withColumn function can be used to create a new DataFrame by applying a UDF (user-defined function) to an existing DataFrame. Option B (select) is used to select one or more columns from a DataFrame, and option C (filter) is used to filter rows from a DataFrame based on a specific condition.
62. Which of the following Spark SQL functions can be used to drop one or more columns from a DataFrame?
A. drop
B. select
C. filter
D. All of the above
Answer: A
Explanation: The drop function can be used to drop one or more columns from a DataFrame. Option B (select) is used to select one or more columns from a DataFrame, and option C (filter) is used to filter rows from a DataFrame based on a specific condition.
63. Which of the following Spark SQL functions can be used to create a new DataFrame by combining two or more existing DataFrames?
A. union
B. join
C. intersect
D. All of the above
Answer: A
Explanation: The union function can be used to create a new DataFrame by combining two or more existing DataFrames. Option B (join) is used to join two DataFrames based on a common column, and option C (intersect) is used to get the intersection of two DataFrames.
64. Which of the following Spark SQL functions can be used to extract a substring from a column in a DataFrame?
A. substring
B. trim
C. lower
D. All of the above
Answer: A
Explanation: The substring function can be used to extract a substring from a column in a DataFrame. Option B (trim) is used to remove leading and trailing spaces from a column, and option C (lower) is used to convert a column to lowercase.
65. Which of the following Spark SQL functions can be used to convert a column to a different data type in a DataFrame?
A. cast
B. convert
C. as
D. All of the above
Answer: A
Explanation: The cast function can be used to convert a column to a different data type in a DataFrame. Option B (convert) is not a valid function in Spark SQL, and option C (as) is used as a syntactic sugar to specify an alias for a column or a table.
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