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Question 76
What is the primary purpose of Delta Lake in Databricks?
A) To provide data visualization capabilities
B) To provide ACID transactions and reliable data lake storage with versioning
C) To manage user authentication and authorization
D) To execute machine learning models
Answer: B
Explanation:
Delta Lake is an open-source storage layer that brings ACID transaction properties, scalable metadata handling, time travel capabilities, and unified batch and streaming data processing to data lakes built on cloud object storage. This foundational technology transforms unreliable data lakes into reliable data lakehouses by adding database-like guarantees and capabilities to files stored in cloud storage systems like AWS S3, Azure Data Lake Storage, or Google Cloud Storage.
The ACID transaction properties ensure atomicity where operations either complete fully or not at all preventing partial writes, consistency maintaining data integrity through schema enforcement and constraint validation, isolation ensuring concurrent operations do not interfere with each other, and durability guaranteeing committed transactions persist despite failures. These properties are critical for enterprise data platforms where data quality, reliability, and consistency directly impact business decisions and regulatory compliance.
Delta Lake implements several key features addressing data lake challenges: schema enforcement and evolution preventing incompatible data writes while allowing controlled schema changes, time travel enabling querying historical versions of data for auditing or rollback, unified batch and streaming processing treating both workloads consistently, efficient upserts and deletes through merge operations avoiding full table rewrites, data versioning maintaining complete change history, and scalable metadata handling using transaction logs rather than expensive directory listings. The technology achieves these capabilities through a transaction log stored alongside data files recording all operations.
Common use cases include building reliable data pipelines with guaranteed data quality, implementing slowly changing dimensions with historical tracking, enabling compliance and audit requirements through data versioning, performing efficient incremental processing, handling late-arriving data in streaming scenarios, and supporting concurrent read and write operations without corruption. Delta Lake has become the default table format in Databricks due to its significant advantages over traditional Parquet files or other formats.
Data visualization uses tools like dashboards and reports. User management uses identity and access management systems. Machine learning execution uses clusters and ML runtimes. Only Delta Lake provides ACID transactions and reliable storage.
Question 77
Which SQL command is used to create a new database in Databricks?
A) MAKE DATABASE
B) CREATE DATABASE
C) NEW DATABASE
D) BUILD DATABASE
Answer: B
Explanation:
The CREATE DATABASE command in Databricks SQL creates a new database schema that serves as a logical container for organizing tables, views, and other database objects. Databases provide namespace isolation allowing multiple teams or projects to use the same table names without conflicts, enable access control at the database level simplifying permissions management, and organize related data assets logically reflecting business domains or application boundaries.
The basic syntax CREATE DATABASE database_name creates a database with the specified name, with optional clauses including IF NOT EXISTS preventing errors when the database already exists, COMMENT adding descriptive metadata explaining the database purpose, LOCATION specifying a custom storage path for database data rather than using default locations, and WITH DBPROPERTIES setting key-value properties for database metadata. Database names must follow naming conventions typically allowing alphanumeric characters and underscores while avoiding reserved keywords.
When a database is created, Databricks establishes a directory structure in the underlying storage location where tables created within the database store their data files. The default database named default exists in all Databricks workspaces providing a namespace for tables created without explicit database specification. Users can switch between databases using the USE database_name command, and qualify table references with database names using database_name.table_name syntax for cross-database queries.
Database-level operations include SHOW DATABASES listing all databases, DESCRIBE DATABASE displaying database properties and location, DROP DATABASE removing databases and optionally their contents, and ALTER DATABASE modifying database properties. Best practices include creating separate databases for different environments like development, staging, and production, using descriptive database names reflecting their contents or purposes, documenting databases with comments, and implementing consistent naming conventions across the organization.
Commands like MAKE DATABASE, NEW DATABASE, and BUILD DATABASE do not exist in standard SQL or Databricks SQL. Only CREATE DATABASE is the correct command for database creation.
Question 78
What is the purpose of the DESCRIBE command in Databricks SQL?
A) To delete table data
B) To display the schema and metadata of a table or view
C) To create documentation files
D) To encrypt table contents
Answer: B
Explanation:
The DESCRIBE command in Databricks SQL displays detailed schema information and metadata about tables, views, databases, or functions, providing essential information for understanding data structure, exploring available data assets, and developing queries. This introspection capability is fundamental for data analysts working with unfamiliar datasets, documenting data structures, and validating schema expectations.
When applied to tables or views, DESCRIBE returns a result set showing each column name, data type, and nullable status along with additional metadata like comments, partitioning columns, and table properties. The command accepts several variations: DESCRIBE table_name provides basic column information, DESCRIBE EXTENDED table_name includes comprehensive metadata such as table format, location, creation time, and detailed statistics, DESCRIBE DETAIL table_name for Delta tables shows additional Delta-specific information including file counts and sizes, and DESCRIBE HISTORY table_name displays the transaction log for Delta tables showing all operations performed.
For databases, DESCRIBE DATABASE database_name shows database properties including location, comment, and custom properties. For functions, DESCRIBE FUNCTION function_name displays function signatures, descriptions, and usage examples. The command also supports describing specific columns using DESCRIBE table_name column_name syntax providing detailed information about individual columns including statistics when available.
The DESCRIBE command is read-only, never modifying data or structure, making it safe to use in production environments. Alternative syntax includes DESC as a shorthand for DESCRIBE. Results from DESCRIBE can inform query development showing available columns and their types, guide data quality assessments identifying nullable columns or unexpected types, support documentation efforts capturing current schema state, and assist troubleshooting by confirming actual structure matches expectations. Integration with notebooks enables capturing DESCRIBE output for analysis or automated documentation generation.
Table data deletion uses DELETE or TRUNCATE commands. Documentation file creation uses external tools or notebook exports. Encryption uses security configurations not DESCRIBE. Only DESCRIBE displays schema and metadata information.
Question 79
Which function is used to convert a string to uppercase in Databricks SQL?
A) UPPERCASE()
B) UPPER()
C) TOUPPER()
D) CAPS()
Answer: B
Explanation:
The UPPER function in Databricks SQL converts all characters in a string to uppercase, providing a standard SQL function for text normalization, case-insensitive comparisons, and data standardization. This function is essential for data cleaning, joining tables with inconsistent case, and formatting output for consistency.
The function accepts a single string argument and returns a new string with all lowercase alphabetic characters converted to uppercase while leaving uppercase characters, numbers, and special characters unchanged. The syntax UPPER(string_expression) applies to column references, string literals, or expressions that evaluate to strings. The function handles NULL values by returning NULL, following standard SQL NULL propagation semantics where operations on NULL values produce NULL results.
Common use cases include standardizing data for comparison where UPPER(column1) = UPPER(column2) performs case-insensitive equality checks, normalizing data during ETL processes converting inconsistent case to uniform uppercase, generating display values requiring uppercase presentation, and implementing case-insensitive grouping or aggregation by converting strings before GROUP BY operations. The function integrates with other string functions enabling complex transformations like UPPER(TRIM(column)) removing whitespace and converting to uppercase in a single expression.
Performance considerations include recognizing that UPPER applies per-row transformations that can impact performance on large datasets, particularly when used in WHERE clauses preventing index usage. For case-insensitive comparisons on frequently queried columns, storing both original and uppercase versions or using case-insensitive collations may provide better performance. The function is locale-aware for international characters, correctly converting accented characters according to Unicode standards.
Functions named UPPERCASE, TOUPPER, and CAPS do not exist in standard SQL or Databricks SQL. The complementary function LOWER converts strings to lowercase. Only UPPER provides uppercase string conversion.
Question 80
What is the purpose of the GROUP BY clause in SQL?
A) To sort query results in ascending order
B) To aggregate rows with the same values in specified columns
C) To filter rows based on conditions
D) To join multiple tables
Answer: B
Explanation:
The GROUP BY clause in SQL aggregates rows that share the same values in specified columns into summary rows, enabling calculation of aggregate statistics like counts, sums, averages, minimums, and maximums for each group. This fundamental SQL capability transforms detailed row-level data into summarized insights, supporting analytical queries that answer questions about patterns, totals, and trends within datasets.
When GROUP BY is specified, the database engine partitions all rows into groups based on unique combinations of values in the grouping columns, then applies aggregate functions to each group separately producing one result row per group. For example, GROUP BY customer_id aggregates all orders for each customer enabling calculations like total purchases per customer, while GROUP BY product_category, region creates groups for each combination of category and region enabling regional product analysis.
The SELECT clause in queries using GROUP BY must reference only columns listed in GROUP BY or aggregate functions, as referencing other columns is ambiguous when multiple detail rows collapse into one summary row. Aggregate functions available include COUNT for counting rows, SUM for totaling numeric values, AVG for calculating means, MIN and MAX for finding extreme values, and various statistical functions. The HAVING clause filters groups after aggregation, complementing WHERE which filters rows before aggregation.
Common use cases include calculating sales totals by time periods, product categories, or sales representatives, computing customer metrics like order counts or average order values, analyzing website traffic by page or user segment, generating summary reports for dashboards and KPIs, and identifying patterns through aggregated metrics. Advanced grouping includes GROUP BY with multiple columns creating hierarchical groupings, ROLLUP and CUBE extensions producing subtotals and grand totals, and GROUPING SETS specifying multiple grouping combinations in one query.
Result sorting uses ORDER BY. Row filtering uses WHERE or HAVING. Table joining uses JOIN clauses. Only GROUP BY aggregates rows by common column values.
Question 81
Which clause is used to filter aggregated results in SQL?
A) WHERE
B) FILTER
C) HAVING
D) SELECT
Answer: C
Explanation:
The HAVING clause in SQL filters groups produced by GROUP BY based on aggregate conditions, operating on summarized data after grouping and aggregation occur. This clause enables filtering based on aggregate calculations like totals, counts, or averages, complementing the WHERE clause which filters individual rows before grouping.
The distinction between WHERE and HAVING is fundamental to SQL query processing: WHERE filters rows before they are grouped and aggregated, operating on individual row values, while HAVING filters groups after aggregation completes, operating on aggregate results. For example, WHERE order_amount > 100 includes only orders exceeding 100 dollars before grouping, while HAVING SUM(order_amount) > 1000 includes only customer groups whose total orders exceed 1000 dollars after grouping all orders per customer.
HAVING conditions use aggregate functions like COUNT, SUM, AVG, MIN, MAX, or other aggregations that produce summary values for groups. The syntax follows GROUP BY clauses and precedes ORDER BY clauses in query structure. Multiple conditions can be combined using AND, OR, and NOT logical operators. Common patterns include HAVING COUNT(*) > 5 finding groups with more than five members, HAVING AVG(value) BETWEEN 10 AND 20 filtering groups by average ranges, and HAVING MAX(date) > current_date() identifying groups with recent activity.
Use cases include identifying high-value customers by filtering for groups with large total purchases, finding popular products by filtering for items with high order counts, detecting anomalies by filtering for groups with unusual aggregate statistics, and generating reports showing only significant groups meeting threshold criteria. Performance considerations include understanding that HAVING evaluates after grouping completes, so filtering with WHERE before grouping when possible reduces data volume earlier in query processing improving efficiency.
WHERE filters pre-aggregation rows. FILTER is not a standard filtering clause. SELECT projects columns. Only HAVING filters post-aggregation grouped results.
Question 82
What is the purpose of the DISTINCT keyword in SQL?
A) To sort results in descending order
B) To remove duplicate rows from query results
C) To create distinct tables
D) To join distinct datasets
Answer: B
Explanation:
The DISTINCT keyword in SQL removes duplicate rows from query results, returning only unique combinations of values across all selected columns. This deduplication capability is essential for understanding unique values within datasets, counting distinct occurrences, and producing cleaner result sets for analysis and reporting.
When DISTINCT appears after SELECT, the database engine evaluates all rows in the result set, identifies rows that are identical across all selected columns, and eliminates duplicates retaining only one instance of each unique combination. For example, SELECT DISTINCT customer_id FROM orders returns each customer ID only once regardless of how many orders that customer placed, while SELECT DISTINCT product_category, region returns unique combinations of categories and regions.
DISTINCT operates on the entire row considering all selected columns together, meaning SELECT DISTINCT col1, col2 returns unique combinations of col1 and col2 values not unique col1 values independently. For counting distinct values, COUNT(DISTINCT column) counts unique values in a column ignoring duplicates and NULL values. DISTINCT can be computationally expensive on large datasets as it requires comparing all rows, sorting or hashing to identify duplicates, making proper indexing and query optimization important.
Common use cases include identifying unique customers, products, or other entities in transactional data, counting distinct occurrences for metrics like unique visitors or distinct product purchases, generating dropdown lists or filter options showing available values, deduplicating data imported from sources with redundancy, and exploring data distributions by examining unique value combinations. Alternative approaches for deduplication include GROUP BY which provides more control and allows aggregation alongside deduplication.
Sorting uses ORDER BY with ASC or DESC. Table creation uses CREATE TABLE. Joining uses JOIN clauses. Only DISTINCT removes duplicate rows from results.
Question 83
Which join type returns all rows from both tables, with NULLs where matches do not exist?
A) INNER JOIN
B) LEFT JOIN
C) RIGHT JOIN
D) FULL OUTER JOIN
Answer: D
Explanation:
The FULL OUTER JOIN, also called simply FULL JOIN, returns all rows from both tables involved in the join, combining rows where join conditions match and including unmatched rows from both sides with NULL values filling in for missing columns. This comprehensive join type ensures no data from either table is excluded, providing complete visibility into both matching and non-matching records.
When a FULL OUTER JOIN executes, the database engine identifies matching rows based on the join condition, includes all matching rows with values from both tables, includes unmatched rows from the left table with NULL values for right table columns, and includes unmatched rows from the right table with NULL values for left table columns. The result set size equals or exceeds the size of both input tables, potentially containing more rows than either table individually when both have unmatched records.
Common use cases include reconciliation tasks identifying records present in one system but missing in another, comprehensive reporting showing all entities regardless of relationship existence, data quality assessment finding orphaned or unmatched records, merging datasets from different sources ensuring no data is lost, and analyzing relationships where both presence and absence of matches provide insights. The join is particularly valuable when investigating data inconsistencies or building master datasets from multiple sources.
Query patterns often include NULL checks in SELECT or WHERE clauses to identify which records came from which side, using expressions like CASE WHEN left_table.id IS NULL THEN ‘Only in right’ WHEN right_table.id IS NULL THEN ‘Only in left’ ELSE ‘In both’ END categorizing rows by match status. Performance considerations include recognizing that FULL OUTER JOIN can produce large result sets and may be more expensive than other join types, particularly on large tables without proper indexing.
INNER JOIN returns only matching rows. LEFT JOIN returns all left table rows plus matches. RIGHT JOIN returns all right table rows plus matches. Only FULL OUTER JOIN returns all rows from both tables.
Question 84
What does the COUNT aggregate function return when applied to a column with NULL values?
A) The total number of rows including NULLs
B) The number of non-NULL values in the column
C) An error message
D) Zero
Answer: B
Explanation:
The COUNT function when applied to a specific column using syntax COUNT(column_name) returns the number of non-NULL values in that column, excluding any rows where the column contains NULL. This behavior is fundamental to understanding SQL aggregate functions and their handling of missing data, as different COUNT syntaxes produce different results based on NULL treatment.
SQL provides several COUNT variations with distinct NULL handling: COUNT() counts all rows in the result set regardless of NULL values in any columns, returning the total row count; COUNT(column_name) counts only rows where the specified column contains non-NULL values, excluding NULLs from the count; COUNT(DISTINCT column_name) counts unique non-NULL values in the column; and COUNT(1) or COUNT with any literal value behaves identically to COUNT() counting all rows.
Understanding this distinction is critical for accurate analysis, particularly when calculating metrics like response rates, completion percentages, or data quality measures. For example, in a survey dataset with optional questions, COUNT(*) shows total survey responses while COUNT(optional_question) shows how many respondents answered that specific question. The difference between these counts reveals the response rate for the optional question.
Common patterns include using both COUNT() and COUNT(column) to calculate NULL percentages with expressions like (COUNT() – COUNT(column)) calculating NULL counts or COUNT(column) / COUNT(*) * 100 calculating the percentage of non-NULL values. These calculations inform data quality assessments, identify columns with significant missing data requiring investigation or imputation, and support completeness metrics for data governance.
COUNT(*) includes NULLs in total row count. The function does not error on NULLs. Zero would be returned only if no non-NULL values exist. Only COUNT(column_name) returns the count of non-NULL values.
Question 85
What is the purpose of the CASE statement in SQL?
A) To create new tables
B) To implement conditional logic returning different values based on conditions
C) To manage database transactions
D) To declare variables
Answer: B
Explanation:
The CASE statement in SQL implements conditional logic within queries, evaluating conditions and returning different values based on which conditions are true, functioning similarly to if-then-else statements in programming languages. This powerful feature enables complex value transformations, categorical classifications, and conditional calculations directly within SQL queries without requiring multiple queries or external processing.
SQL supports two CASE syntax forms: simple CASE that compares an expression against multiple values using syntax CASE expression WHEN value1 THEN result1 WHEN value2 THEN result2 ELSE default_result END, and searched CASE that evaluates multiple boolean conditions using syntax CASE WHEN condition1 THEN result1 WHEN condition2 THEN result2 ELSE default_result END. The searched form provides more flexibility handling complex conditions, ranges, and multiple column comparisons.
The CASE statement evaluates WHEN clauses sequentially from top to bottom, returning the result associated with the first condition that evaluates to true and skipping subsequent conditions. If no conditions match and an ELSE clause exists, the ELSE result is returned; if no ELSE clause exists and no conditions match, NULL is returned. CASE can appear anywhere expressions are valid including SELECT lists, WHERE conditions, ORDER BY clauses, and aggregate function arguments.
Common applications include categorizing continuous values into bins or ranges like CASE WHEN age < 18 THEN ‘Minor’ WHEN age < 65 THEN ‘Adult’ ELSE ‘Senior’ END, handling NULL values by replacing them with default values, implementing business logic directly in queries such as calculating discounts based on purchase amounts or customer tiers, pivoting data by using CASE within aggregate functions, and creating derived flags or indicators based on multiple column conditions. The statement is essential for transforming data during retrieval without modifying source data.
Table creation uses CREATE TABLE. Transaction management uses BEGIN, COMMIT, ROLLBACK. Variable declaration syntax varies by database. Only CASE implements conditional logic in queries.
Question 86
Which SQL clause is used to sort query results?
A) GROUP BY
B) ORDER BY
C) SORT BY
D) ARRANGE BY
Answer: B
Explanation:
The ORDER BY clause in SQL sorts query results based on one or more columns or expressions, controlling the order in which rows appear in the result set. Sorting is essential for presenting data logically, finding top or bottom values, and preparing data for sequential processing or reporting.
The ORDER BY clause appears at the end of SQL queries after SELECT, FROM, WHERE, GROUP BY, and HAVING clauses. The basic syntax ORDER BY column_name sorts results by the specified column in ascending order by default. Multiple sort columns can be specified using comma separation as in ORDER BY column1, column2 where results are first sorted by column1, then rows with identical column1 values are sorted by column2 providing hierarchical sorting.
Sort direction is controlled by ASC for ascending order (default if not specified) or DESC for descending order, applied to each sort column independently. For example, ORDER BY date DESC, amount ASC sorts by date from newest to oldest, then within each date sorts amounts from smallest to largest. NULL values are treated according to database-specific rules, typically appearing first in ascending sorts or last in descending sorts, with some databases providing NULLS FIRST or NULLS LAST modifiers for explicit control.
Advanced sorting includes ordering by expressions like ORDER BY LENGTH(description) sorting by calculated values, ordering by column positions using numbers like ORDER BY 1, 2 referring to first and second SELECT columns though this practice is discouraged for maintainability, and ordering by aggregate results in queries with GROUP BY. Performance considerations include recognizing that sorting large result sets can be expensive, and creating appropriate indexes on frequently sorted columns can dramatically improve query performance.
GROUP BY aggregates rows. SORT BY is not standard SQL though exists in some systems. ARRANGE BY does not exist. Only ORDER BY provides standard SQL result sorting.
Question 87
What is the purpose of the UNION operator in SQL?
A) To join tables horizontally
B) To combine result sets from multiple queries vertically, removing duplicates
C) To create database unions
D) To merge table schemas
Answer: B
Explanation:
The UNION operator in SQL combines result sets from two or more SELECT queries into a single result set, stacking rows vertically and automatically removing duplicate rows that appear in multiple queries. This set operation enables combining data from different tables with similar structures, merging results from different query conditions, and creating comprehensive result sets from disparate sources.
For UNION to work correctly, all SELECT statements combined must return the same number of columns, corresponding columns must have compatible data types allowing implicit conversion, and column names in the final result set are taken from the first SELECT statement. The operator performs deduplication comparing complete rows and keeping only unique combinations, which requires sorting or hashing operations that can impact performance on large result sets.
The UNION ALL variant provides an alternative that includes all rows from all queries without removing duplicates, offering better performance when duplicates are not a concern or when queries are known to produce distinct results. Common patterns include using UNION to combine historical and current data tables, merging active and archived records, consolidating regional data from multiple sources, or combining results from different conditions that are expensive to express as a single query.
Best practices include ensuring consistent column ordering and naming across all SELECT statements, using column aliases to provide meaningful names in the final result, considering UNION ALL when duplicates are acceptable for performance benefits, and indexing appropriately on source tables as each SELECT in the UNION executes independently. The operator can combine more than two queries, with syntax SELECT… UNION SELECT… UNION SELECT… chaining multiple queries together.
Horizontal joins use JOIN operators. Database unions are not a concept. Schema merging is structural not query-level. Only UNION combines query results vertically removing duplicates.
Question 88
What is a view in Databricks SQL?
A) A physical table that stores data
B) A saved query that appears as a virtual table
C) A data visualization chart
D) A user interface component
Answer: B
Explanation:
A view in Databricks SQL is a saved query definition that appears and can be queried like a table but does not physically store data, instead executing the underlying query dynamically each time the view is referenced. Views provide abstraction layers over physical tables, simplifying complex queries, securing data through controlled access, and providing consistent interfaces even when underlying table structures change.
Views are created using CREATE VIEW view_name AS SELECT… syntax, where the SELECT statement defines the view’s structure and content. When queries reference views, the database engine substitutes the view definition into the query, executing the underlying SELECT statement against the base tables. This dynamic execution means views always reflect current data in underlying tables without requiring synchronization or refresh operations, but also means view queries incur the computational cost of the underlying SELECT statement each time they execute.
Common use cases include simplifying complex joins, aggregations, or transformations by encapsulating them in reusable views, implementing row or column-level security by creating views that filter sensitive data based on user permissions, providing backwards compatibility when table structures change by creating views with old column names mapping to new structures, creating business logic layers that enforce calculation rules or data transformations consistently, and improving query readability by replacing complex subqueries with named views.
Views can reference other views creating layered abstractions, though deep nesting can impact performance and complicate troubleshooting. Materialized views, created with CREATE MATERIALIZED VIEW, physically store query results and refresh periodically providing better query performance at the cost of storage space and potential data staleness. Views can be temporary existing only for the session duration, or permanent persisting in the database catalog. Security considerations include understanding that view query execution uses the privileges of the querying user not the view creator unless explicitly configured otherwise.
Physical storage uses tables. Data visualizations are dashboards or charts. User interfaces are separate from database views. Only views provide virtual table abstractions over saved queries.
Question 89
What is the purpose of window functions in SQL?
A) To create graphical windows for data display
B) To perform calculations across related rows while maintaining individual row detail
C) To manage multiple database sessions
D) To define time windows for data retention
Answer: B
Explanation:
Window functions in SQL perform calculations across sets of rows related to the current row while maintaining the individuality of each row in the result set, unlike aggregate functions which collapse multiple rows into summary results. This capability enables sophisticated analytical calculations such as running totals, moving averages, rankings, and row comparisons that are difficult or impossible to express using standard aggregation.
Window functions are defined using the OVER clause which specifies the window of rows for calculation. The window can include PARTITION BY dividing rows into groups similar to GROUP BY but without collapsing rows, ORDER BY defining the sequence for calculations like running totals or rankings, and frame specifications like ROWS BETWEEN defining the exact window of rows for calculations relative to the current row. The syntax function_name() OVER (PARTITION BY… ORDER BY… frame_specification) provides flexible control over calculation scope.
Common window functions include ranking functions like ROW_NUMBER assigning sequential numbers, RANK and DENSE_RANK assigning ranks with different tie-handling, aggregate functions like SUM, AVG, COUNT operating over windows to produce running totals or moving averages, analytical functions like LEAD and LAG accessing values from subsequent or previous rows, FIRST_VALUE and LAST_VALUE retrieving boundary values, and statistical functions calculating percentiles or distributions.
Use cases include calculating running totals for cumulative metrics, computing moving averages for trend analysis, ranking items within categories for top-N analysis, comparing current row values to previous or next rows for change detection, calculating percentiles and quartiles for distribution analysis, and performing complex time-series analyses. Window functions execute after WHERE, GROUP BY, and HAVING but before ORDER BY in query processing order, enabling them to reference aggregated values when combined with GROUP BY.
Graphical windows are user interface elements. Session management uses connection handling. Data retention uses policies and lifecycle management. Only window functions perform calculations across row sets maintaining row-level detail.
Question 90
What is the purpose of the EXPLAIN command in Databricks SQL?
A) To provide help documentation for SQL commands
B) To show the execution plan for a query
C) To generate data dictionaries
D) To create query explanations for end users
Answer: B
Explanation:
The EXPLAIN command in Databricks SQL displays the execution plan that the query optimizer generates for a SQL statement, showing the sequence of operations, data access methods, join strategies, and other implementation details that will be used when the query executes. This visibility into query execution is essential for performance tuning, understanding why queries run slowly, and optimizing query structure or indexing strategies.
When EXPLAIN precedes a SQL statement like EXPLAIN SELECT… the database returns the execution plan instead of query results. The plan shows operations as a tree structure with operations like table scans indicating full table reads, index scans for indexed access, join types like hash join or merge join, sort operations, aggregation methods, and data exchange operations for distributed execution. Each operation includes estimated costs, row counts, and other statistics helping identify expensive operations.
Databricks provides several EXPLAIN variants: EXPLAIN produces a textual execution plan, EXPLAIN EXTENDED includes additional details like physical and logical plans, EXPLAIN CODEGEN shows generated code for query execution, and EXPLAIN COST includes detailed cost estimates. Analyzing execution plans helps identify problems like full table scans on large tables suggesting missing indexes, inefficient join orders or strategies indicating statistics updates needed, unnecessary sorting or data shuffling suggesting query restructuring opportunities, and predicate pushdown failures indicating optimization opportunities.
Performance tuning workflow typically includes running EXPLAIN before query optimization, identifying expensive operations, applying optimizations like adding filters to reduce data scanned, restructuring joins to process smaller datasets first, ensuring statistics are current for accurate cost estimation, then running EXPLAIN again to verify improvements. Understanding execution plans requires familiarity with database internals and distributed computing concepts like data partitioning and shuffle operations, but even basic plan analysis can identify obvious performance issues.
Help documentation uses HELP or documentation resources. Data dictionaries use DESCRIBE or information schema. User-facing explanations are created manually. Only EXPLAIN shows query execution plans for optimization.