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Question 181:
Which function extracts JSON fields from string columns?
A) json_extract()
B) get_json_object()
C) extract_json()
D) parse_json()
Answer: B
Explanation:
The get_json_object() function extracts specific fields from JSON-formatted string columns using JSONPath expressions, enabling targeted extraction without parsing entire JSON structures. This function is useful when you need only specific fields from JSON strings, avoiding the overhead of parsing complete JSON into structured columns. It returns extracted values as strings, which can then be cast to appropriate types.
Usage requires the JSON string column and JSONPath expression: df.withColumn(“name”, get_json_object(col(“json_col”), “.user.name”))extractsthenamefieldfromnestedJSONstructure.JSONPathexpressionsusedollarsignforroot,dotsforfieldaccess,andbracketsforarrayindexing:”.user.name”)) extracts the name field from nested JSON structure. JSONPath expressions use dollar sign for root, dots for field access, and brackets for array indexing: ” .user.name”))extractsthenamefieldfromnestedJSONstructure.JSONPathexpressionsusedollarsignforroot,dotsforfieldaccess,andbracketsforarrayindexing:”.items[0].price” accesses the price of the first item. The function returns null for non-existent paths or malformed JSON.
Get_json_object() is more efficient than from_json() when you need only a few fields from large JSON strings, avoiding parsing overhead for unused portions. However, for extensive JSON manipulation or when accessing many fields, from_json() with schema specification is generally more maintainable and efficient. Choose based on how much of the JSON structure you need to access.
A, C, and D use incorrect function names that don’t exist in Spark’s SQL functions. The actual function is get_json_object(), clearly indicating its purpose of retrieving objects from JSON.
Common use cases include extracting specific fields from JSON logs, parsing API response strings, accessing nested JSON attributes without full schema parsing, and implementing lightweight JSON field extraction. For complex JSON processing with many fields, consider from_json() which creates structured columns. For simple field extraction, get_json_object() provides targeted, efficient access without schema definitions.
Question 182:
What is the purpose of the transform() method for arrays?
A) To reshape arrays
B) To apply a function to each array element
C) To convert array types
D) To transpose arrays
Answer: B
Explanation:
The transform() method for arrays applies a lambda function to each element within array columns, transforming array contents element-wise without exploding arrays into rows. This higher-order function enables complex array manipulations while maintaining array structure, providing efficient element-wise transformations that would otherwise require explosion, transformation, and re-collection. Transform() preserves array lengths and operates independently on each array in the DataFrame.
Usage requires a lambda expression defining the transformation: df.withColumn(“doubled”, transform(col(“numbers”), lambda x: x * 2)) doubles every element in the numbers array. The lambda receives each array element as input and returns the transformed value. More complex expressions can reference element position: transform(col(“arr”), (x, i) => x + i) adds the index to each element.
Higher-order functions like transform() enable sophisticated array operations without the performance overhead of exploding arrays. Exploding creates one row per array element, dramatically increasing dataset size and requiring subsequent aggregation to reconstruct arrays. Transform() operates directly on array structures, maintaining efficiency for element-wise operations on arrays with many elements.
A incorrectly suggests structural reshaping rather than element transformation. C misunderstands transform as type conversion rather than value transformation. D is completely wrong as transform doesn’t transpose or reorganize array structure.
Common use cases include normalizing values within arrays, applying mathematical operations to numeric arrays, cleaning or formatting string arrays, implementing element-wise calculations without explosion, and transforming nested array structures. Related higher-order array functions include filter() for arrays which selects elements meeting conditions, and aggregate() which reduces arrays to single values. Understanding these functions enables efficient array manipulation in nested data structures.
Question 183:
Which storage level uses off-heap memory?
A) MEMORY_ONLY
B) OFF_HEAP
C) EXTERNAL_MEMORY
D) HEAP_MEMORY
Answer: B
Explanation:
The OFF_HEAP storage level stores cached data in off-heap memory, which is memory outside the JVM heap managed directly by the operating system. This storage approach avoids Java garbage collection overhead that can cause unpredictable pauses with large heap-based caches. Off-heap storage requires explicit memory management but provides more predictable performance characteristics, especially for very large cached datasets where garbage collection pauses would be problematic.
Using off-heap storage requires configuring Spark to enable off-heap memory and allocate sufficient space: spark.memory.offHeap.enabled=true and spark.memory.offHeap.size settings control off-heap allocation. Once configured, df.persist(StorageLevel.OFF_HEAP) caches data in this memory region. Off-heap cached data doesn’t contribute to JVM heap pressure, preventing garbage collection issues that plague large in-heap caches.
Off-heap storage is particularly valuable for applications caching very large datasets where JVM heap limits become constraints or where garbage collection pauses impact latency-sensitive applications. The approach trades some access speed for predictable performance without GC pauses. Off-heap memory management is more complex than heap-based caching, requiring careful configuration and monitoring.
A uses standard JVM heap memory, subject to garbage collection. C and D are not actual storage level names in Spark. OFF_HEAP is the specific storage level for off-heap memory caching.
Understanding off-heap caching benefits applications with large memory footprints where garbage collection becomes problematic. Modern JVMs handle garbage collection well for moderate heap sizes, but multi-gigabyte heaps can experience long GC pauses. Off-heap memory sidesteps this issue entirely. Monitor memory usage carefully when using off-heap storage to ensure sufficient memory allocation without over-committing resources. Balance benefits against added configuration complexity.
Question 184:
What is the purpose of the json_tuple() function?
A) To create JSON tuples
B) To extract multiple fields from JSON strings efficiently
C) To validate JSON structure
D) To compare JSON objects
Answer: B
Explanation:
The json_tuple() function extracts multiple fields from JSON-formatted string columns in a single operation, providing better performance than multiple get_json_object() calls when extracting several fields. This function is optimized for scenarios where you need multiple fields from the same JSON string, parsing the JSON once and extracting all requested fields simultaneously. It returns multiple columns, one for each requested field.
Usage specifies the JSON column followed by field names to extract: df.select(col(“id”), json_tuple(col(“json_data”), “name”, “age”, “city”)) extracts three fields creating three new columns. Unlike get_json_object() which returns values directly with column names you specify, json_tuple() creates columns named c0, c1, c2, etc., requiring aliases for readable names: json_tuple(col(“json”), “field1”, “field2”).alias(“f1”, “f2”).
Json_tuple() is more efficient than multiple get_json_object() calls because it parses JSON once rather than repeatedly for each field extraction. This efficiency matters when extracting many fields from large JSON strings or processing high-volume data. However, json_tuple() only handles top-level fields, not nested paths, limiting its applicability compared to get_json_object() which supports full JSONPath expressions.
A misunderstands the function as creating JSON rather than extracting from it. C incorrectly suggests validation rather than extraction. D is wrong as the function doesn’t compare JSON objects.
Common use cases include efficiently extracting multiple top-level fields from JSON logs, parsing JSON API responses when several fields are needed, and improving performance over repeated get_json_object() calls. For nested field access, use get_json_object(). For complex JSON with schemas, use from_json(). Choose json_tuple() specifically when extracting multiple top-level fields where its efficiency optimization provides value.
Question 185:
Which method is used to stop a streaming query?
A) stop()
B) terminate()
C) halt()
D) end()
Answer: A
Explanation:
The stop() method terminates a running streaming query, halting data processing and cleaning up resources. Streaming queries run continuously once started, requiring explicit termination through stop() when you want to shut down processing gracefully. This method allows controlled shutdown, ensuring in-flight micro-batches complete before stopping, maintaining data consistency and proper resource cleanup.
Usage involves calling stop() on the StreamingQuery object returned when starting a streaming query: query = df.writeStream.format(“console”).start(); query.stop() starts then stops the query. In production, streaming queries typically run indefinitely, stopped only during maintenance, application shutdown, or error conditions. The stop() method blocks until the query fully terminates, ensuring clean shutdown before proceeding.
Proper streaming query lifecycle management requires handling startup, monitoring, and graceful shutdown. Start queries with writeStream().start(), monitor using query.status and query.recentProgress properties, handle exceptions appropriately, and stop gracefully with stop() during shutdown. This lifecycle management ensures reliable streaming applications that can be deployed, monitored, and maintained in production environments.
B, C, and D use incorrect method names. Spark’s streaming API uses stop() consistently with other Spark APIs for terminating operations.
Understanding streaming query management enables building production-ready streaming applications. Handle query failures through exception handlers, implement monitoring to detect issues, and ensure graceful shutdown during application termination. Streaming queries maintain state and checkpoints that require proper cleanup. The stop() method ensures resources are released, connections closed, and state properly finalized. Always implement proper shutdown hooks in production streaming applications.
Question 186:
What is the purpose of the collect_set() aggregation function?
A) To collect all values into arrays
B) To collect unique values into arrays removing duplicates
C) To aggregate sets mathematically
D) To validate value sets
Answer: B
Explanation:
The collect_set() aggregation function collects unique values from a column into arrays within each group, automatically removing duplicates. This function is similar to collect_list() but ensures each value appears only once in the resulting array, making it ideal for scenarios where uniqueness matters. The order of elements in the result array is not guaranteed since set semantics don’t imply ordering.
Usage in aggregations: df.groupBy(“customer_id”).agg(collect_set(“product_category”).alias(“categories”)) creates arrays of unique product categories purchased by each customer. If a customer bought multiple products in the same category, that category appears once in the array. This automatic deduplication simplifies queries that need unique collections without explicit distinct operations before aggregation.
Collect_set() is valuable when aggregating dimensions or attributes where duplicates are meaningless or misleading. Collecting unique tags, categories, locations, or identifiers naturally uses set semantics. The function handles nulls by excluding them from results, similar to other aggregation functions. For cases where duplicate occurrences matter, use collect_list() instead to preserve all values including duplicates.
A describes collect_list() which preserves duplicates. C completely misunderstands the function as mathematical set operations. D is incorrect as the function aggregates rather than validates.
Common use cases include aggregating unique attributes per entity, creating arrays of distinct associated values, preparing deduplicated collections for downstream processing, and implementing set-based analytics where duplicate occurrences are irrelevant. Choose collect_set() over collect_list() when uniqueness is semantically appropriate. Be cautious with very high-cardinality columns that might produce extremely large arrays. Consider whether collecting into arrays is appropriate or if alternative aggregations better suit your needs.
Question 187:
Which function converts arrays to delimited strings?
A) array_to_string()
B) array_join()
C) join_array()
D) stringify_array()
Answer: B
Explanation:
The array_join() function converts array columns to delimited strings by concatenating array elements with a specified separator. This function is essential for creating human-readable string representations of arrays, preparing data for export to systems expecting delimited strings, or generating formatted output. It handles nulls within arrays gracefully, with options for how to represent null elements.
Usage requires the array column and delimiter: df.withColumn(“tags_str”, array_join(col(“tags”), “,”)) creates comma-separated strings from tag arrays. Optional third parameter specifies null replacement: array_join(col(“array”), “,”, “NULL”) replaces null elements with “NULL” string instead of skipping them. Without null replacement, nulls are omitted from the output string.
Array_join() is particularly useful when preparing data for external systems that expect delimited strings rather than complex array types, creating readable concatenated representations for display or reporting, or flattening array structures into simple text formats. The function is the inverse of split() which converts delimited strings into arrays, together enabling bidirectional conversion between arrays and delimited string representations.
A, C, and D use incorrect function names. The standard Spark function is array_join(), clearly indicating its purpose of joining array elements into strings.
Common use cases include exporting data to CSV where array columns need flattening, creating comma-separated lists for display, generating formatted strings from arrays for logging or reporting, and preparing data for systems that don’t support complex types. Choose appropriate delimiters based on data content to avoid ambiguity. Consider whether elements might contain the delimiter character, potentially requiring escaping or alternative delimiters. Understanding bidirectional conversions between arrays and strings enables flexible data format manipulation.
Question 188:
What is the purpose of the map_keys() function?
A) To create map keys
B) To extract all keys from map columns as arrays
C) To map column keys
D) To index map structures
Answer: B
Explanation:
The map_keys() function extracts all keys from map-typed columns, returning arrays containing every key present in each map. This function enables analyzing map structures, accessing map keys for iteration or filtering, and understanding what keys exist in map collections. The resulting array contains keys in arbitrary order since maps don’t guarantee key ordering.
Usage is straightforward: df.withColumn(“property_names”, map_keys(col(“properties”))) extracts all keys from the properties map into an array. For null maps, the function returns null. The extracted key array can then be used with other array functions like array_contains() to check for specific keys, or size() to count how many key-value pairs exist in each map.
Map_keys() is particularly valuable when working with schema-less or flexible data where map columns store arbitrary key-value pairs. Understanding what keys exist, filtering based on key presence, or iterating over keys all rely on map_keys(). The complementary map_values() function extracts all values, while map_entries() returns arrays of key-value structs for comprehensive map examination.
A misunderstands the function as creating keys rather than extracting existing ones. C incorrectly suggests mapping operations rather than key extraction. D confuses key extraction with indexing operations.
Common use cases include analyzing schema variability in flexible map structures, filtering maps based on key presence using array_contains() on extracted keys, understanding data completeness by examining which keys exist, and implementing logic that depends on map structure. For accessing specific key values rather than all keys, use element_at() or bracket notation. Understanding map functions enables effective manipulation of key-value structures common in JSON and semi-structured data.
Question 189:
Which method is used to write data with partitioning?
A) writePartitioned()
B) partitionBy() before write()
C) write() with partitioning option
D) partitionWrite()
Answer: B
Explanation:
The partitionBy() method is used before write operations to organize output data into partitioned directory structures based on column values. This method creates hierarchical subdirectories for each unique combination of partition column values, dramatically improving query performance by enabling partition pruning where Spark skips entire directories that don’t match filter predicates. Partitioning is fundamental to efficient data lake organization.
Usage chains partitionBy() before format specification: df.write.partitionBy(“year”, “month”).parquet(path) creates directory structure like path/year=2024/month=01/, path/year=2024/month=02/ with data files within each partition directory. Multiple partition columns create nested hierarchies. Spark automatically encodes partition values in directory names, embedding metadata in file system structure that accelerates filtered queries.
Choosing appropriate partition columns requires understanding query patterns and data distribution. Partition by columns frequently used in WHERE clauses to enable partition pruning. Avoid high-cardinality columns that create excessive directories, as too many small files degrade performance. Ideal partition columns have moderate cardinality, align with common query filters, and create roughly balanced partition sizes.
A, C, and D reference non-existent methods or options. Spark uses partitionBy() as a method in the write chain, clearly indicating its partitioning purpose.
Common partitioning patterns include temporal partitioning by date components for time-series data, geographic partitioning by region or country for location-based queries, and categorical partitioning by department, product type, or status. Proper partitioning can reduce query times by orders of magnitude by reading only relevant partitions. Monitor partition counts and sizes to maintain optimal balance between granularity and file count overhead.
Question 190:
What is the purpose of the slice() function for arrays?
A) To split arrays
B) To extract a subset of array elements by position
C) To divide arrays into slices
D) To sample array elements
Answer: B
Explanation:
The slice() function extracts a contiguous subset of elements from array columns based on starting position and length, returning new arrays containing only the specified range. This function enables extracting specific portions of arrays without converting to rows, maintaining array structure while accessing only needed elements. Positions are 1-indexed in Spark SQL, with position 1 referring to the first element.
Usage specifies the array column, start position, and length: df.withColumn(“first_three”, slice(col(“items”), 1, 3)) extracts the first three elements from the items array. Negative positions count from the end: slice(col(“arr”), -2, 2) extracts the last two elements. If requested length exceeds available elements, slice() returns as many elements as exist without errors.
Slice() is valuable when working with arrays where only specific positions matter, extracting prefixes or suffixes from arrays, or implementing windowing logic within arrays. Combined with other array functions, it enables sophisticated array manipulations without explosion. For example, extracting recent items from time-ordered arrays, accessing fixed-position elements in structured arrays, or implementing array pagination logic.
A incorrectly suggests splitting arrays into multiple arrays rather than extracting a single contiguous subset. C is similar misunderstanding implying multiple outputs. D confuses slicing with random sampling, which would be different functionality.
Common use cases include extracting top N elements from sorted arrays, accessing fixed-position elements in structured data, implementing array windowing or pagination, and working with prefixes or suffixes of arrays. For non-contiguous element extraction, combine multiple slice() calls or use filter() on arrays. Understanding array slicing enables efficient array subset operations without expensive explosion and re-aggregation patterns.
Question 191:
Which function computes the standard deviation of a column?
A) std()
B) stddev()
C) standard_deviation()
D) Both A and B
Answer: D
Explanation:
Both std() and stddev() compute the standard deviation of numeric columns, and they are functionally identical in Spark. These aggregation functions calculate sample standard deviation using the n-1 denominator, providing an unbiased estimator of population standard deviation. Standard deviation measures spread or variability in data, indicating how much values typically deviate from the mean.
Usage in aggregations: df.agg(stddev(“salary”)) or df.agg(std(“salary”)) both calculate salary standard deviation across all rows. For grouped calculations: df.groupBy(“department”).agg(stddev(“salary”)) computes standard deviation per department. The functions ignore null values, computing statistics only on non-null data. For population standard deviation using n denominator, use stddev_pop() instead.
Standard deviation is fundamental to statistical analysis, measuring data dispersion around the mean. High standard deviation indicates values spread widely, while low values indicate clustering near the mean. This metric is essential for understanding data distributions, identifying outliers, calculating confidence intervals, and assessing variability in business metrics or experimental results.
C uses incorrect function naming. Spark provides both std() and stddev() for convenience, accommodating different naming preferences.
Common use cases include measuring variability in business metrics, assessing data quality through dispersion analysis, calculating statistical process control limits, understanding risk through return volatility, and identifying unusual variability patterns. Standard deviation complements mean to provide comprehensive distribution summaries. For skewed distributions, consider additional metrics like median and interquartile range. Understanding dispersion measures alongside central tendency enables thorough data characterization.
Question 192:
What is the purpose of the spark.sql() method?
A) To validate SQL syntax
B) To execute SQL queries and return DataFrames
C) To format SQL strings
D) To configure SQL settings
Answer: B
Explanation:
The spark.sql() method executes SQL query strings against registered tables and views, returning results as DataFrames. This method enables using SQL syntax for data manipulation and analysis while remaining within Spark’s DataFrame ecosystem. Queries can reference any tables or views registered in the Spark catalog, including temporary views created from DataFrames and permanent tables in metastores.
Usage accepts SQL strings: spark.sql(“SELECT * FROM employees WHERE salary > 80000”) executes the query and returns a DataFrame with results. The SQL dialect supports most standard SQL features including joins, subqueries, window functions, and complex expressions. Results are DataFrames that can be further processed using DataFrame API methods, enabling seamless mixing of SQL and programmatic approaches.
Spark.sql() provides a powerful way to leverage SQL expertise, migrate existing SQL queries to Spark, or use SQL syntax when it’s more expressive than DataFrame methods. Complex queries with multiple joins and subqueries are often more readable in SQL. The Catalyst optimizer applies to SQL queries just as it does to DataFrame operations, ensuring equivalent performance regardless of which API you use.
A misunderstands the method as validation rather than execution. C incorrectly suggests formatting rather than executing queries. D confuses executing SQL with configuring SQL settings, which uses different configuration methods.
Common use cases include executing existing SQL queries in Spark, using SQL for complex analytics that read naturally in SQL syntax, enabling SQL-proficient analysts to work with Spark data, and migrating SQL-based workflows to distributed processing. Combine spark.sql() with temporary view registration to bridge between DataFrame operations and SQL queries. Understanding both APIs enables choosing the most appropriate interface for each task.
Question 193:
Which function checks if values are in a specified list?
A) in_list()
B) isin()
C) contains_any()
D) in_array()
Answer: B
Explanation:
The isin() function checks if column values exist within a specified list of values, returning a boolean column indicating membership. This function provides efficient filtering based on value membership in sets, implementing SQL IN clause functionality within DataFrame operations. It’s essential for filtering based on multiple discrete values without complex OR conditions.
Usage specifies values to match: df.filter(col(“status”).isin(“active”, “pending”, “approved”)) selects rows where status is any of the specified values. The function accepts any number of arguments, each representing a value to match. For programmatic value lists: df.filter(col(“id”).isin(*id_list)) unpacks a Python list into arguments.
Isin() is more efficient and readable than chaining multiple equality checks with OR operators. It clearly expresses set membership intent and allows Spark to optimize the filtering operation. The function works with any comparable data type including strings, numbers, and dates. For very large value lists, consider whether filtering approach or join with reference data might be more appropriate.
A, C, and D use incorrect function names. The DataFrame Column API uses isin() following common programming language conventions for membership testing.
Common use cases include filtering for specific categories, selecting records matching approved values, implementing whitelist or blacklist logic, and filtering based on dynamically generated value sets. For array columns checking if they contain specific values, use array_contains() instead. For checking if column values exist in another DataFrame, use joins or broadcasting. Understanding membership testing enables efficient filtering without verbose OR chains.
Question 194:
What is the purpose of the format_number() function?
A) To convert strings to numbers
B) To format numeric values with thousands separators and decimals
C) To generate random numbers
D) To validate number formats
Answer: B
Explanation:
The format_number() function formats numeric columns as strings with thousands separators and specified decimal places, creating human-readable number representations for display or reporting. This function is essential for preparing numeric data for presentation, generating formatted reports, or creating user-facing output where readability matters more than computational precision.
Usage requires the numeric column and decimal places: df.withColumn(“formatted_salary”, format_number(col(“salary”), 2)) creates strings like “75,000.00” from numeric salary values. The function adds commas as thousands separators and rounds to the specified decimal precision. For integers without decimals, use 0 decimal places: format_number(col(“count”), 0) produces “1,234” format.
Format_number() is purely for display formatting, converting numbers to strings that cannot be used in mathematical operations without conversion back. Use it as final step before output when creating reports, dashboards, or any user-facing numeric displays. For computations, maintain numeric types; format only when presenting results.
A describes casting or parsing functions that convert strings to numeric types, the opposite of format_number(). C is completely unrelated to formatting. D misunderstands the function as validation rather than formatting.
Common use cases include formatting financial values for reports, creating readable large number displays, preparing data for export to presentation systems, and generating formatted output for dashboards. The function implements locale-specific formatting conventions common in financial and business contexts. Understanding formatting functions enables creating professional, readable outputs from raw numeric data while maintaining computational accuracy in processing pipelines.
Question 195:
Which method is used to get the underlying RDD from a DataFrame?
A) getRDD()
B) rdd
C) toRDD()
D) asRDD()
Answer: B
Explanation:
The rdd property accesses the underlying RDD from a DataFrame, enabling interoperability between DataFrame and RDD APIs. This property returns an RDD of Row objects, where each Row contains the values from one DataFrame row. Accessing the underlying RDD allows using lower-level RDD operations when DataFrame API limitations require it, though DataFrame operations should be preferred for their optimization benefits.
Usage simply accesses the property: df.rdd returns the RDD[Row]. You can then apply RDD transformations: df.rdd.map(lambda row: (row[‘name’], row[‘age’])) processes rows using RDD API. Converting to RDD enables fine-grained control, custom partitioning, or operations not available in DataFrame API, but sacrifices Catalyst optimizer benefits.
Understanding when to drop to RDD level requires balancing control against optimization. Use DataFrames whenever possible for automatic optimization, type safety with Datasets in Scala, and cleaner, more maintainable code. Use RDD access only when necessary for operations unavailable in DataFrame API, custom partitioning requirements, or integration with RDD-based libraries.
A, C, and D use incorrect method or property names. Spark exposes the underlying RDD through a simple property named rdd, not through methods.
Common use cases include accessing RDD-specific operations like mapPartitionsWithIndex(), implementing custom partitioning not available through DataFrame API, using RDD-based libraries that haven’t migrated to DataFrame APIs, and performing low-level optimizations when DataFrame API doesn’t provide necessary control. Always consider whether DataFrame approaches can achieve your goals before dropping to RDD level, as RDD operations bypass Catalyst optimization.
Question 196:
What is the purpose of the variance() function?
A) To calculate data variety
B) To compute statistical variance of numeric columns
C) To measure value ranges
D) To validate data consistency
Answer: B
Explanation:
The variance() function computes the statistical variance of numeric columns, measuring data spread by calculating the average squared deviation from the mean. Variance quantifies dispersion, with higher values indicating greater spread and lower values indicating clustering near the mean. This aggregation function provides sample variance using n-1 denominator for unbiased population variance estimation.
Usage in aggregations: df.agg(variance(“score”)) calculates score variance across all rows. For grouped calculations: df.groupBy(“class”).agg(variance(“score”)) computes variance per class. The function ignores null values, computing statistics only on non-null data. Variance is the square of standard deviation, so variance() and stddev() are mathematically related.
Variance is fundamental to statistics, appearing in ANOVA, regression analysis, hypothesis testing, and quality control. While standard deviation is often more interpretable due to being in original units, variance has mathematical properties that make it preferable for certain analyses. Understanding variance enables comprehensive statistical data analysis.
A completely misunderstands variance as diversity or variety rather than statistical dispersion. C incorrectly suggests range measurement, which would be max minus min. D wrongly interprets variance as validation rather than dispersion measurement.
Common use cases include statistical hypothesis testing requiring variance calculations, quality control monitoring variability, regression analysis using variance partitioning, financial risk assessment through return variance, and statistical process control. Variance measures are sensitive to outliers since deviations are squared. For robust dispersion measures, consider median absolute deviation or interquartile range. Understanding variance alongside mean provides comprehensive distribution summaries essential for statistical inference.
Question 197:
Which function reverses the order of array elements?
A) array_reverse()
B) reverse()
C) flip_array()
D) invert_array()
Answer: B
Explanation:
The reverse() function reverses the order of elements in array columns,returning new arrays with elements in opposite order. This function is useful for reordering array contents, accessing elements from the end first, or implementing specific business logic requiring reversed sequences. The operation maintains all array elements while simply changing their order from last-to-first.
Usage is straightforward: df.withColumn(“reversed_items”, reverse(col(“items”))) creates arrays with reversed element order. If the original array is [1, 2, 3, 4], the reversed array becomes [4, 3, 2, 1]. The function handles empty arrays and nulls gracefully, returning empty arrays for empty inputs and null for null inputs.
Reverse() is valuable when array element order matters and you need to process from end to beginning. Combined with slice() or element_at(), you can access elements from the end without knowing array length. For example, reverse() followed by slice() to get the last N elements, then reverse again to restore original order within that subset.
A uses incorrect function naming. Spark’s array function is simply reverse(), following common programming conventions. C and D are not actual Spark functions.
Common use cases include accessing recent items in time-ordered arrays without complex indexing, implementing last-in-first-out logic, reordering arrays for specific business requirements, and accessing tail elements efficiently. For accessing single elements from the end, element_at() with negative indices might be more efficient than reversing entire arrays. Understanding array manipulation functions enables flexible nested data processing without expensive explosion operations.
Question 198:
What is the purpose of the map_filter() function?
A) To filter DataFrames by maps
B) To filter map entries based on a condition
C) To create filtered maps
D) To validate map contents
Answer: B
Explanation:
The map_filter() function filters entries in map-typed columns based on a lambda expression condition, returning new maps containing only key-value pairs that satisfy the predicate. This higher-order function enables conditional map manipulation while maintaining map structure, providing efficient filtering without converting maps to arrays or exploding into rows.
Usage requires a lambda expression that receives key and value: df.withColumn(“filtered_props”, map_filter(col(“properties”), lambda k, v: v > 100)) retains only map entries where values exceed 100. The lambda receives each key-value pair and returns boolean indicating whether to include that entry. Keys and values can both be referenced in filter conditions.
Map_filter() is part of Spark’s higher-order functions for complex types, enabling sophisticated operations on nested structures. Filtering maps based on value conditions, key patterns, or combinations enables cleaning map data, removing irrelevant entries, or implementing business rules on flexible key-value structures without complex UDFs or explosions.
A misunderstands the function as DataFrame filtering rather than map content filtering. C incorrectly suggests map creation rather than filtering existing maps. D is wrong as the function filters rather than validates.
Common use cases include removing map entries that don’t meet criteria, cleaning maps by filtering out invalid or irrelevant key-value pairs, implementing conditional map processing, and preparing maps for downstream operations by removing unwanted entries. Higher-order functions for maps, arrays, and structs enable complex nested data manipulation that would be difficult with traditional approaches. Understanding these functions is essential for effective semi-structured data processing.
Question 199:
Which method is used to get the execution plan explanation?
A) showPlan()
B) explain()
C) displayPlan()
D) getPlan()
Answer: B
Explanation:
The explain() method displays the execution plan for a DataFrame, showing how Spark will execute the transformations when an action is called. This method is crucial for understanding query optimization, debugging performance issues, and learning how Catalyst optimizer transforms logical operations into physical execution steps. The output reveals optimization techniques applied and provides insights into query execution.
Usage varies by detail level: df.explain() shows physical plan, df.explain(True) shows extended information including parsed logical plan, analyzed logical plan, optimized logical plan, and physical plan. The extended output traces optimization stages, revealing how Spark transforms your high-level operations into low-level execution steps. This visibility enables understanding and validating optimizations.
Understanding execution plans helps identify performance bottlenecks like unnecessary shuffles, verify that predicate pushdown works correctly, confirm join strategies match expectations, and understand stage boundaries. The physical plan shows actual operators that will execute, including Exchange operators representing shuffles, Scan operators showing data source reads, and join implementation details.
A, C, and D use incorrect method names. Spark’s standard method for displaying execution plans is explain(), following SQL conventions.
Common use cases include debugging slow queries by examining execution plans, verifying optimization techniques are applied, understanding shuffle operations and their causes, learning how Spark optimizes different operations, and validating that queries execute as intended. Look for Exchange nodes indicating shuffles, Scan nodes showing data source reads and pushed predicates, and join strategies like BroadcastHashJoin or SortMergeJoin. The explain output directly correlates with Spark UI visualizations, providing complementary perspectives on query execution.
Question 200:
What is the purpose of the shuffle() function?
A) To randomly reorder DataFrame rows
B) To randomly reorder array elements
C) To redistribute data across partitions
D) To mix column values
Answer: B
Explanation:
The shuffle() function randomly reorders elements within array columns, returning new arrays with elements in random order. This function is useful for randomizing array contents, implementing sampling from arrays, or creating random orderings within arrays for specific algorithms or applications. Each array is shuffled independently, with different random orders per row.
Usage is simple: df.withColumn(“shuffled_items”, shuffle(col(“items”))) creates arrays with randomly ordered elements. For reproducible results across executions, provide a seed: shuffle(col(“array”), seed=42) generates the same random order given the same seed. Without a seed, the shuffling is non-deterministic, producing different orders on each execution.
Shuffle() operates on individual arrays within rows, distinct from data shuffling across cluster partitions which is a different concept. This function is purely for randomizing array element order, useful in scenarios like randomly selecting from array elements, implementing random sampling within arrays, or creating randomized test data with array fields.
A incorrectly describes DataFrame row reordering, which would use orderBy(rand()). C confuses array shuffling with partition shuffling, which is a different operation related to data distribution. D wrongly suggests mixing values across columns.
Common use cases include implementing random selection from arrays, creating randomized orderings for experiments or simulations, testing with randomly ordered array data, and implementing algorithms requiring random element access patterns. For DataFrame row shuffling, use orderBy(rand()). For partition redistribution, use repartition(). Understanding the distinction between array shuffling and data shuffling prevents confusion between these different randomization concepts.
