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Question 106:
Which SQL command is used to create a new table in Databricks?
A) CREATE TABLE
B) MAKE TABLE
C) NEW TABLE
D) INSERT TABLE
Answer: A
Explanation:
CREATE TABLE is the standard SQL command for creating new tables in Databricks, defining table structure including column names, data types, constraints, and storage properties. This fundamental DDL (Data Definition Language) statement establishes the schema and physical storage for organizing data in structured format within the Databricks lakehouse architecture.
The basic syntax includes CREATE TABLE followed by the table name, then column definitions enclosed in parentheses specifying column names and data types. Optional clauses add functionality including USING to specify file format like DELTA, PARQUET, or CSV, LOCATION to define storage path, PARTITIONED BY to organize data for query performance, and various table properties controlling behavior.
Delta Lake tables represent the recommended table type in Databricks providing ACID transactions, time travel, schema enforcement, and performance optimizations. Creating Delta tables uses CREATE TABLE with USING DELTA clause or simply omits the USING clause as Delta is the default format. Delta tables support both managed tables where Databricks controls storage and external tables pointing to existing data locations.
Advanced options include CREATE TABLE AS SELECT (CTAS) creating tables from query results, CREATE TABLE LIKE copying structure from existing tables without data, and CREATE TABLE IF NOT EXISTS preventing errors when tables already exist. Temporary tables using CREATE TEMP TABLE provide session-scoped tables for intermediate processing.
Column definitions support various data types including primitive types like STRING, INT, DOUBLE, BOOLEAN, DATE, and TIMESTAMP, and complex types like ARRAY, MAP, and STRUCT for nested data. Constraints include NOT NULL preventing null values and COMMENT adding documentation. Understanding CREATE TABLE syntax is essential for building data models and organizing datasets within Databricks environments.
Question 107:
What is the purpose of the DESCRIBE command in Databricks SQL?
A) To display the schema and metadata of a table
B) To delete table contents
C) To create table descriptions
D) To modify table structure
Answer: A
Explanation:
The DESCRIBE command displays table schema and metadata information including column names, data types, nullability, and various table properties. This metadata inspection capability enables analysts to understand table structure, verify column definitions, and access important table information without querying actual data or examining external documentation.
Command variations include DESCRIBE TABLE for basic column information showing column names and data types in tabular format, DESCRIBE EXTENDED providing comprehensive metadata including storage location, table properties, partition columns, and statistics, and DESCRIBE FORMATTED offering detailed formatted output with all available table metadata organized into sections.
The output structure presents column information first listing each column with its data type and nullability status. For Delta tables, additional information includes partition columns, table properties like delta.minReaderVersion and delta.minWriterVersion, storage location showing where data physically resides, and table provider indicating the table format.
Common use cases include verifying table schema before writing queries ensuring column names and types match expectations, understanding partition structure for query optimization, locating table storage paths for external access or debugging, and examining table properties controlling behavior like change data feed enablement or deletion vectors.
Alternative commands include SHOW COLUMNS displaying only column names and types in simplified format, and SHOW TBLPROPERTIES listing table properties without full metadata. The DESCRIBE family of commands does not modify tables, delete data, or create descriptions. These commands specifically provide read-only metadata inspection essential for understanding data structures and table configurations.
Question 108:
Which function is used to extract the year from a date column in Databricks SQL?
A) YEAR()
B) EXTRACT_YEAR()
C) GET_YEAR()
D) DATE_YEAR()
Answer: A
Explanation:
The YEAR function extracts the year component from date or timestamp columns returning a four-digit integer representing the year. This date extraction function enables temporal analysis, date-based filtering, and time-series aggregations by isolating specific date components from complete datetime values.
Function syntax accepts date or timestamp expressions as input automatically handling various date formats and timestamp precision. The function returns integer values representing years like 2024, 2023, or earlier years. NULL input produces NULL output following standard SQL null handling conventions. The function integrates naturally into SELECT clauses, WHERE conditions, and GROUP BY operations.
Related date extraction functions include MONTH extracting month numbers from 1 to 12, DAY extracting day of month from 1 to 31, HOUR, MINUTE, and SECOND for time components, and DAYOFWEEK returning day numbers where Sunday equals 1. These functions work together enabling comprehensive temporal data manipulation and analysis.
Common patterns include temporal aggregation using GROUP BY YEAR to calculate annual metrics, date filtering using WHERE YEAR equals specific year for year-based data extraction, and derived columns creating year fields for visualization and reporting. The function simplifies date arithmetic compared to string parsing or manual calculation approaches.
Alternative extraction uses the EXTRACT function with syntax like EXTRACT(YEAR FROM date_column) providing SQL standard compliant extraction. Both approaches produce identical results with YEAR function offering more concise syntax. Understanding date functions is essential for time-based analysis common in business intelligence, trend analysis, and temporal reporting scenarios.
Question 109:
What does the MERGE statement do in Databricks SQL?
A) Combines results from multiple SELECT queries
B) Performs INSERT, UPDATE, and DELETE operations based on conditions in a single statement
C) Merges multiple tables into one
D) Joins two tables together
Answer: B
Explanation:
The MERGE statement performs conditional INSERT, UPDATE, and DELETE operations in a single atomic transaction based on matching conditions between source and target tables. This powerful DML statement enables complex data synchronization patterns including upserts (update-or-insert), slowly changing dimensions, and incremental data loading common in modern data pipelines.
The statement structure includes MERGE INTO specifying the target table, USING clause providing the source data which can be a table, view, or subquery, ON condition defining matching logic between source and target, and WHEN clauses specifying actions for matched and unmatched rows. Multiple WHEN clauses handle different scenarios enabling sophisticated data merge logic.
Common patterns include WHEN MATCHED THEN UPDATE updating existing target rows when source matches, WHEN NOT MATCHED THEN INSERT adding new rows from source that don’t exist in target, and WHEN NOT MATCHED BY SOURCE THEN DELETE removing target rows without corresponding source entries. Conditional clauses using AND filters enable selective actions based on additional criteria.
Delta Lake provides optimized MERGE implementation with ACID transaction guarantees ensuring consistency during complex multi-row operations. The operation scales efficiently handling large datasets through optimized execution plans and predicate pushdown. Merge operations automatically manage Delta Lake transaction log maintaining table history and enabling time travel.
UNION combines query results rather than modifying data. Table consolidation uses different approaches. JOIN creates result sets from multiple tables without modification. The MERGE statement specifically provides conditional multi-operation data modification essential for maintaining synchronized datasets, implementing change data capture, and managing incremental updates in production data pipelines.
Question 110:
Which visualization type is best for showing the distribution of a continuous variable?
A) Pie chart
B) Histogram
C) Line chart
D) Bar chart
Answer: B
Explanation:
Histograms effectively display continuous variable distributions by grouping values into bins and showing the frequency or count of observations within each bin. This visualization reveals data distribution shape, central tendency, spread, and patterns including normal distributions, skewness, multimodal distributions, and outliers essential for exploratory data analysis and statistical understanding.
Histogram construction divides the continuous variable range into equal-width intervals called bins then counts observations falling within each bin. The x-axis represents the variable values organized into bins while the y-axis shows frequencies or counts. Adjacent bars touch indicating the continuous nature of the underlying variable unlike bar charts where gaps separate discrete categories.
Distribution insights from histograms include identifying normal distributions with symmetric bell curves, detecting skewness where data concentrates toward one end, recognizing multimodal distributions with multiple peaks suggesting distinct subpopulations, and spotting outliers appearing as isolated bars far from main distribution. Bin width selection impacts visualization effectiveness requiring balance between detail and clarity.
Databricks SQL Analytics and notebooks support histogram creation through built-in visualization tools. Analysts select histogram visualization types, specify the continuous variable for analysis, configure bin counts or widths, and optionally add grouping for overlaid distributions. The visualizations render interactively enabling exploration and insight discovery.
Pie charts display categorical proportions rather than continuous distributions. Line charts show trends over time. Bar charts compare discrete categories. Histograms specifically address continuous distribution visualization providing essential statistical insight into variable characteristics, normality assumptions, and data quality essential for quantitative analysis and data-driven decision making.
Question 111:
What is the purpose of the WHERE clause in a SQL query?
A) To sort results
B) To filter rows based on specified conditions
C) To group results
D) To join tables
Answer: B
Explanation:
The WHERE clause filters rows based on specified conditions determining which rows from tables or views are included in query results. This fundamental SQL component enables selective data retrieval, improves query performance by reducing processed data volume, and implements business logic by applying filtering criteria matching analytical requirements.
Clause syntax follows SELECT and FROM clauses containing boolean expressions that evaluate to true, false, or null for each row. Rows where the condition evaluates to true are included in results while false or null rows are excluded. Conditions use comparison operators like equals, greater than, less than, and not equal, logical operators AND, OR, and NOT for combining conditions, and various predicates including IN, BETWEEN, LIKE, and IS NULL.
Common filtering patterns include equality filters selecting specific values, range filters using comparison operators or BETWEEN for continuous values, pattern matching using LIKE with wildcards for text searches, set membership using IN with value lists, and null handling using IS NULL or IS NOT NULL. Complex conditions combine multiple predicates creating sophisticated filtering logic.
Performance considerations make WHERE clause optimization critical where appropriate filters reduce data scanned improving query speed and reducing compute costs. Delta Lake predicate pushdown leverages WHERE conditions to skip irrelevant data files during reads. Partition column filters in WHERE clauses enable partition pruning dramatically improving query performance for partitioned tables.
ORDER BY sorts results rather than filtering. GROUP BY aggregates rows. JOIN combines tables. The WHERE clause specifically provides row filtering capability essential for targeted data retrieval, analytical constraints, and query optimization fundamental to SQL data analysis and reporting.
Question 112:
Which aggregate function calculates the average of numeric values?
A) SUM()
B) AVG()
C) MEAN()
D) AVERAGE()
Answer: B
Explanation:
The AVG aggregate function calculates the arithmetic mean of numeric values by summing all non-null values and dividing by the count of non-null values. This fundamental statistical measure provides central tendency understanding essential for quantitative analysis, performance metrics, and comparative analytics across business domains.
Function behavior handles null values automatically by excluding them from both the sum calculation and count denominator. This null-handling approach prevents skewed results and provides intuitive behavior where missing values don’t contribute to averages. For columns containing only nulls, AVG returns null rather than zero or error.
Usage patterns include column-level aggregation calculating overall averages across all rows like AVG(revenue) for mean revenue, grouped aggregation combining AVG with GROUP BY for category-specific means, and conditional aggregation using CASE expressions within AVG for filtered averages. The function accepts numeric types including integers, decimals, and floating-point values.
Common analytical applications include calculating average transaction values, mean customer ages, typical order sizes, average processing times, and benchmark metrics. Comparing averages across segments reveals performance differences and guides decision-making. Time-series averages identify trends and seasonal patterns.
Alternative statistical functions include SUM totaling values without averaging, COUNT counting observations, MIN and MAX finding extreme values, and MEDIAN finding middle values. MEAN and AVERAGE are not standard SQL function names though some systems provide them as aliases. The AVG function specifically provides arithmetic mean calculation essential for descriptive statistics and business intelligence across analytical use cases.
Question 113:
What is the purpose of a Common Table Expression (CTE) in Databricks SQL?
A) To create permanent tables
B) To define temporary named result sets for use within a query
C) To delete data from tables
D) To modify table schemas
Answer: B
Explanation:
Common Table Expressions (CTEs) define temporary named result sets that exist only during query execution providing readable, maintainable, and reusable query components. This SQL feature enables breaking complex queries into logical steps, improving code clarity, facilitating debugging, and supporting recursive operations without creating permanent database objects.
CTE syntax begins with the WITH keyword followed by CTE name, optional column list, AS keyword, and query definition enclosed in parentheses. Multiple CTEs can be defined in sequence separated by commas creating chains of derived datasets. The main query following CTE definitions can reference CTEs by name as if they were tables enabling composition of complex analytics from simpler components.
Readability benefits include logical organization where complex queries split into named steps describing data transformations clearly, self-documenting code where CTE names explain intermediate result purposes, and simplified debugging enabling inspection of individual CTE results during development. These advantages make CTEs preferable to nested subqueries for complex analytics.
Performance characteristics vary where CTEs typically materialize once even when referenced multiple times in some databases, though Databricks may optimize CTE evaluation through query rewriting. Temporary tables provide alternative approaches for intermediate results when multiple queries need access or when explicit control over materialization is required.
CTEs don’t create permanent objects, delete data, or modify schemas. Those operations use different SQL statements. CTEs specifically provide query-scoped temporary result sets essential for building complex analytical queries with clear logical structure enabling sophisticated data analysis while maintaining code readability and maintainability.
Question 114:
Which JOIN type returns all rows from both tables, matching rows where possible?
A) INNER JOIN
B) LEFT JOIN
C) RIGHT JOIN
D) FULL OUTER JOIN
Answer: D
Explanation:
FULL OUTER JOIN returns all rows from both tables regardless of whether matching rows exist in the other table, combining LEFT JOIN and RIGHT JOIN behavior. This comprehensive join type ensures no data loss from either table while indicating through nulls where matches don’t exist, enabling complete dataset comparison and gap analysis.
Join behavior includes returning matched rows with values from both tables similar to INNER JOIN, returning unmatched rows from the left table with nulls for right table columns similar to LEFT JOIN, and returning unmatched rows from the right table with nulls for left table columns similar to RIGHT JOIN. The result set represents the union of all possible combinations.
Use cases include data quality analysis identifying records existing in one dataset but not another, complete data reconciliation ensuring all records from both sources appear in analysis, and comprehensive reporting requiring visibility into all data regardless of match status. The join type reveals data completeness and consistency issues.
Null handling becomes important where unmatched rows contain nulls in columns from the non-matching table. Analysts must handle nulls appropriately in calculations, filters, and aggregations using functions like COALESCE or IS NULL conditions. Understanding which nulls indicate missing matches versus true null values requires careful analysis.
INNER JOIN returns only matched rows. LEFT JOIN returns all left table rows with matched right rows. RIGHT JOIN returns all right table rows with matched left rows. FULL OUTER JOIN specifically returns all rows from both tables providing comprehensive join coverage essential for complete data analysis and reconciliation scenarios.
Question 115:
What does the DISTINCT keyword do in a SELECT statement?
A) Sorts the results
B) Removes duplicate rows from the result set
C) Counts the number of rows
D) Groups rows together
Answer: B
Explanation:
The DISTINCT keyword removes duplicate rows from query results returning only unique combinations of selected columns. This deduplication functionality enables counting unique values, identifying distinct categories, and eliminating redundancy in result sets ensuring each unique combination appears exactly once.
Keyword placement occurs immediately after SELECT and before column specifications affecting all selected columns together. Databricks evaluates uniqueness across the entire row meaning rows are considered duplicates only when all selected columns match. Single column DISTINCT finds unique values for that column while multiple column DISTINCT finds unique combinations.
Common patterns include counting distinct values using COUNT(DISTINCT column) for cardinality analysis, listing unique categories retrieving all distinct category values without repetition, and deduplicating result sets ensuring analysis operates on unique records. DISTINCT proves essential for understanding data diversity and preventing double-counting in aggregations.
Performance considerations arise as deduplication requires comparing rows to identify duplicates potentially requiring sorts or hash operations. For large datasets, DISTINCT operations can be expensive. Alternative approaches include GROUP BY which provides similar deduplication while enabling aggregation, or using window functions with ROW_NUMBER for more complex deduplication logic based on ordering criteria.
DISTINCT does not sort results though sort operations may occur during deduplication. ORDER BY explicitly controls sorting. COUNT aggregates rows. GROUP BY groups for aggregation. DISTINCT specifically eliminates duplicate rows providing unique result sets essential for accurate counting, category identification, and preventing redundancy in analytical outputs.
Question 116:
Which function converts a string to uppercase in Databricks SQL?
A) UPPER()
B) UPPERCASE()
C) TOUPPER()
D) CAPS()
Answer: A
Explanation:
The UPPER function converts all characters in a string to uppercase letters returning a new string with lowercase letters transformed while leaving uppercase letters, numbers, and special characters unchanged. This text transformation function enables case-insensitive comparisons, standardized formatting, and consistent text processing in data analysis workflows.
Function syntax accepts string expressions including literal strings, column references, or expressions producing string values. The transformation processes each character independently converting lowercase letters to corresponding uppercase equivalents. Non-alphabetic characters including numbers, punctuation, and special characters remain unchanged. Null inputs produce null outputs.
Common use cases include case-insensitive filtering using WHERE UPPER(column) equals UPPER(search_term) matching regardless of case differences, data standardization transforming mixed-case input to consistent uppercase for storage or comparison, and text processing preparing strings for operations requiring case uniformity. The function supports both ASCII and Unicode characters.
Related string functions include LOWER converting to lowercase providing the inverse operation, INITCAP capitalizing the first letter of each word for title case formatting, and TRIM, LTRIM, RTRIM removing whitespace. String concatenation using concat or pipe operators combines these functions creating complex text transformations.
Alternative function names like UPPERCASE, TOUPPER, or CAPS don’t exist in standard SQL though some systems may provide aliases. The UPPER function follows SQL standard naming conventions providing portable code across database systems. Understanding text functions is essential for data cleaning, standardization, and text analysis common in data preparation and quality assurance workflows.
Question 117:
What is the purpose of the HAVING clause in SQL?
A) To filter rows before aggregation
B) To filter groups after aggregation
C) To join tables
D) To sort results
Answer: B
Explanation:
The HAVING clause filters groups created by GROUP BY based on aggregate conditions applied after grouping and aggregation occur. This post-aggregation filtering enables selecting groups meeting specific criteria like minimum counts, threshold sums, or average ranges that cannot be evaluated before aggregation completes.
Clause placement follows GROUP BY and precedes ORDER BY in query structure reflecting its execution timing after grouping. Conditions in HAVING typically reference aggregate functions like COUNT, SUM, AVG, MAX, or MIN evaluating group-level metrics rather than individual row values. This distinguishes HAVING from WHERE which filters individual rows before grouping.
Common patterns include minimum group size filtering using HAVING COUNT(*) greater than threshold excluding small groups, metric threshold filtering using HAVING SUM(amount) greater than target identifying high-value groups, and statistical filtering using HAVING AVG(score) between range selecting groups within average ranges. These filters implement business logic operating on aggregated data.
Performance optimization benefits from appropriate filter placement where row-level conditions belong in WHERE reducing groups created, while group-level conditions belong in HAVING operating on aggregated results. Misplacing filters can impact performance and correctness. Understanding execution order prevents logical errors and optimizes query efficiency.
WHERE filters rows before aggregation. JOIN combines tables. ORDER BY sorts results. HAVING specifically filters groups after aggregation providing essential capability for analytical queries requiring group-level filtering based on aggregate metrics common in business intelligence, reporting, and data analysis applications.
Question 118:
Which SQL clause is used to sort query results?
A) SORT BY
B) ORDER BY
C) ARRANGE BY
D) SEQUENCE BY
Answer: B
Explanation:
The ORDER BY clause sorts query results based on one or more columns or expressions controlling result presentation order. This fundamental SQL component enables organizing data for readability, identifying top or bottom performers, and preparing data for analytical consumption where specific ordering provides meaningful insights.
Clause syntax specifies sort columns or expressions followed by optional direction keywords ASC for ascending (default) or DESC for descending order. Multiple sort columns create hierarchical sorting where secondary columns break ties from primary columns. Expression-based sorting enables ordering by calculated values, functions results, or complex logic.
Sort direction options include ascending order placing smaller values first (numbers low to high, dates early to late, strings A to Z) and descending order reversing this sequence. NULL values typically appear first in ascending sorts and last in descending sorts though behavior can vary. Explicit NULLS FIRST or NULLS LAST clauses control null positioning.
Common patterns include top-N analysis using ORDER BY with LIMIT retrieving highest or lowest values, ranked reporting sorting by metrics to identify leaders or laggards, chronological ordering using date/timestamp columns for time-series analysis, and alphabetical sorting for categorical data presentation. Sorting enhances result interpretability and usability.
Performance considerations arise as sorting requires processing entire result sets potentially impacting large query performance. Indexed columns sort more efficiently. Partition-level ordering using DISTRIBUTE BY and SORT BY in distributed queries optimizes parallel processing. Understanding sorting mechanics enables efficient query design.
Question 119:
What is the purpose of the GROUP BY clause?
A) To filter rows
B) To aggregate rows that share common values in specified columns
C) To sort results
D) To join tables
Answer: B
Explanation:
The GROUP BY clause aggregates rows sharing common values in specified columns into summary groups enabling calculation of aggregate statistics like counts, sums, averages, and extremes for each group. This fundamental aggregation mechanism supports analytical patterns including category summaries, time-period totals, and dimensional analysis essential for business intelligence and reporting.
Clause mechanics organize rows into groups where all rows with identical values in GROUP BY columns form a single group. Aggregate functions in the SELECT clause then calculate statistics across rows within each group producing one output row per group. Non-aggregated columns in SELECT must appear in GROUP BY unless they are functionally dependent on grouped columns.
Common aggregation patterns include category analysis using GROUP BY on categorical columns calculating metrics per category, time-series aggregation using GROUP BY on date components like year or month for temporal analysis, multi-dimensional analysis using GROUP BY on multiple columns for cross-tabular reporting, and hierarchical rollups using GROUPING SETS, CUBE, or ROLLUP for multi-level summaries.
Aggregate functions compatible with GROUP BY include COUNT counting group members, SUM totaling numeric values, AVG calculating means, MIN and MAX finding extremes, and statistical functions like STDDEV and VARIANCE. These functions ignore null values except COUNT(*) which counts all rows.
WHERE filters rows before grouping. ORDER BY sorts results. JOIN combines tables. GROUP BY specifically enables aggregation by organizing rows into groups for summary calculation providing essential functionality for analytical queries requiring category summaries, trend analysis, and dimensional reporting fundamental to data analysis and business intelligence.
Question 120:
Which data type is used to store true/false values in Databricks?
A) BINARY
B) BOOLEAN
C) BIT
D) FLAG
Answer: B
Explanation:
The BOOLEAN data type stores true/false logical values representing binary states, conditions, or flags. This fundamental data type enables logical operations, conditional expressions, and binary classification essential for filtering, decision logic, and categorical analysis in data processing and analytics workflows.
Boolean values include TRUE representing positive, yes, or on states, FALSE representing negative, no, or off states, and NULL representing unknown or missing logical values. Boolean literals use keywords TRUE and FALSE in SQL expressions. Boolean columns accept these three possible values following standard SQL null handling conventions.
Common use cases include flag columns indicating record states like is_active, is_deleted, or is_premium, conditional logic in CASE expressions evaluating boolean conditions, filtering using WHERE clauses with boolean column comparisons, and derived calculations creating boolean columns from comparisons or logical operations. Boolean columns provide memory-efficient binary state storage.
Boolean operations include AND combining conditions requiring all to be true, OR requiring at least one true condition, and NOT inverting boolean values. Comparison operators produce boolean results enabling complex logical expressions. Functions like COALESCE handle null boolean values converting them to explicit true or false values when needed.
Alternative data types like BINARY store byte sequences rather than logical values, BIT exists in some databases but not standard Databricks SQL, and FLAG is not a standard data type. The BOOLEAN data type specifically provides logical value storage following SQL standards essential for representing binary states, implementing decision logic, and supporting conditional operations throughout analytical applications.