Databricks-Spark-Assoc Using Spark SQL Practice Test 1 — 15 Questions

Question 1

You have a Delta table named 'sales_data'. Which TWO SQL commands allow you to effectively manage data storage and query performance for this table?

Accepted Answer

OPTIMIZE sales_data. Managing Delta tables requires proactive maintenance. Z-Ordering is a technique to colocate related information in the same set of files, significantly improving data skipping during query execution. The OPTIMIZE command is essential for consolidating small files into larger ones. Together, these commands form the cornerstone of Delta Lake table maintenance, ensuring that the storage layer remains performant as datasets grow over time, which is essential for professional-grade Spark SQL optimization.

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VACUUM sales_data RETAIN 0 HOURS

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ALTER TABLE sales_data REFRESH

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TRUNCATE TABLE sales_data

Question 2

Which THREE of the following are valid ways to create a Delta table in Spark SQL?

Accepted Answer

CREATE TABLE table_name USING DELTA AS SELECT * FROM source_table. Delta Lake supports multiple ways to initialize tables, providing flexibility for different architectural patterns. Whether migrating existing data, streaming new data, or defining a new schema from scratch, these methods ensure compatibility with ACID transactions. Mastering these creation patterns is essential for Spark developers to build robust ingestion pipelines and maintain high-quality data assets in the Lakehouse, ensuring consistent performance and data integrity across various business use cases.

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CREATE TABLE table_name AS SELECT * FROM path.`/data/path`

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CREATE TABLE table_name FROM '/path/to/files'

Question 3

You are querying a large Delta table and notice that your query is scanning significantly more data than expected. Which technique allows you to restrict the data scanned by the query based on column values?

Accepted Answer

Use the ZORDER BY clause during the table write process.. Data skipping is a primary performance feature of Delta Lake. When you filter by columns that are part of the table's physical layout—either through Z-Ordering or simple partitioning—Spark can skip entire files that do not contain relevant data. This reduces I/O dramatically. Understanding how to leverage predicates in WHERE clauses to facilitate data skipping is vital for writing efficient Spark SQL queries against large-scale, high-volume datasets.

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Implement column-level partitioning on high-cardinality columns.

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Increase the 'spark.sql.broadcastTimeout' configuration.

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Rewrite the query to use a LATERAL VIEW explode.

Question 4

You need to update existing records in a Delta table based on a match from another DataFrame. Which TWO commands or techniques achieve this efficiently?

Accepted Answer

MERGE INTO target_table t USING source_df s ON t.id = s.id WHEN MATCHED THEN UPDATE SET *. The MERGE INTO statement is the standard for performing UPSERT (update/insert) operations in Delta Lake. It is highly optimized, ensuring atomic operations that handle inserts, updates, and deletes in a single pass. Understanding MERGE and the associated logic is a cornerstone of building reliable CDC (Change Data Capture) pipelines. These techniques ensure data consistency and accuracy, which are mandatory requirements in production-grade Spark SQL data engineering workflows.

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UPDATE target_table SET value = x WHERE id IN (SELECT id FROM source_df)

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DELETE FROM target_table WHERE id IN (SELECT id FROM source_df); INSERT INTO target_table SELECT * FROM source_df

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REPLACE INTO target_table SELECT * FROM source_df

Question 5

Which configuration setting is most important to tune when you observe that your Spark SQL job is failing due to 'Out of Memory' errors during a massive join operation?

Accepted Answer

spark.sql.shuffle.partitions. Memory management is central to Spark performance. When joins exceed available executor memory, the shuffle process fails. Adjusting the number of shuffle partitions ensures that each task handles a manageable amount of data. Properly tuning this parameter is essential for preventing job failures, as it balances the degree of parallelism with the memory footprint per task, which is a core responsibility for Spark developers dealing with large-scale data transformations.

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spark.driver.memory

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spark.sql.sources.fileCacheSize

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spark.sql.autoBroadcastJoinThreshold

Question 6

Which SQL function is used to convert a string representation of a date into a DATE type in Spark SQL?

Accepted Answer

CAST(date_string AS DATE). The TO_DATE function is the industry-standard tool for data type conversion in SQL. Dealing with raw data often requires casting strings to proper date objects to enable temporal filtering and grouping. As a Spark developer, mastery of type casting functions is vital to ensure that your data schemas are clean and your analytical queries can leverage time-series logic effectively without runtime conversion errors.

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PARSE_DATE(date_string)

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CONVERT_DATE(date_string)

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STRING_TO_DATE(date_string)

Question 7

You are optimizing a Spark SQL query that performs an equi-join between a 10GB table and a 5MB lookup table. To maximize performance and avoid a shuffle, which action should you take?

Accepted Answer

Use the broadcast hint for the 5MB table in the JOIN clause.. Broadcasting the small table allows Spark to send the entire 5MB dataset to every executor, enabling a map-side join. This strategy eliminates the need to shuffle the 10GB table across the cluster, which is the primary bottleneck in large joins. Efficient join strategies like broadcast joins are essential for Databricks developers to minimize network I/O and reduce overall job execution time in distributed environments.

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Apply a repartition operation to both tables to balance the data distribution.

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Increase the number of partitions to 1000 to improve parallelism during the join.

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Convert both tables to temporary views and join them using standard SQL.

Question 8

Which TWO of the following statements regarding the Spark SQL 'DataFrame.write' operation are accurate?

Accepted Answer

The 'overwrite' mode replaces the entire contents of the destination table or directory.. Understanding write modes and partitioning is critical for managing data lifecycles in Databricks. 'Overwrite' mode replaces existing data, while 'Append' adds to it. Proper partitioning optimizes downstream reads, but excessive partitioning leads to the small file problem. Developers must balance these operations to ensure performant data pipelines that avoid storage overhead and maintain data integrity during write processes to cloud object storage.

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Partitioning by a column with high cardinality is recommended to improve read performance.

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The 'append' mode will fail if the destination directory does not exist.

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Using 'overwrite' mode automatically updates all dependent views.

Question 9

Which of the following functions is best suited for extracting the year from a timestamp column in Spark SQL?

Accepted Answer

YEAR(timestamp_column). Using built-in date-time functions is a standard practice in Spark SQL for data transformation. These functions are highly optimized and pushed down to the storage layer when possible, providing better performance than UDFs. Knowing the correct function name is essential for developers to write concise and efficient code for time-series analysis and reporting within Databricks environments.

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GET_YEAR(timestamp_column)

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EXTRACT_YEAR(timestamp_column)

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DATE_PART(timestamp_column, 'YEAR')

Question 10

Which THREE of the following are valid ways to improve the performance of a Spark SQL query that scans a large partitioned table?

Accepted Answer

Use partition pruning by filtering on the partition column in the WHERE clause.. Performance tuning in Spark SQL often relies on minimizing data scanned from storage. By using partition pruning, columnar formats like Parquet, and predicate pushdown, developers ensure the cluster only processes necessary data. These techniques reduce CPU, I/O, and memory consumption, which are the primary costs of running Databricks workloads, and directly impact job completion times and cost efficiency.

Answer

Increase the number of executors to 1000 for every small query.

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Convert the table to a CSV format to make it easier for Spark to parse.

Question 11

Which function is the most appropriate way to convert a string column containing date information into a proper DateType column in Spark SQL?

Accepted Answer

TO_DATE(col, 'yyyy-MM-dd'). The TO_DATE function is the standard SQL-compliant way to parse date strings into DateType objects. Understanding type casting is fundamental in Spark SQL because improper types lead to inefficient storage and potential runtime errors during aggregation or temporal filtering. Using the correct casting function ensures that the Catalyst optimizer can apply predicate pushdown efficiently, as date-based filters require semantic understanding of temporal data formats to operate correctly.

Answer

CAST(col AS DATE)

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FROM_UNIXTIME(col)

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DATE_FORMAT(col, 'yyyy-MM-dd')

Question 12

You are working with a Delta table. Which TWO of the following commands are valid ways to improve query performance for frequently filtered columns in Spark SQL?

Accepted Answer

OPTIMIZE table_name ZORDER BY (column_name). Optimizing data layout is a core responsibility of a Databricks engineer. Z-Ordering and partitioning are the two primary mechanisms used to skip data during scan operations. By physically organizing data on disk according to frequently queried columns, Spark can prune large portions of the dataset that do not meet filter criteria, drastically reducing I/O and accelerating query response times across large-scale data lakes.

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REORG TABLE table_name APPLY (PURGE)

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VACUUM table_name RETAIN 0 HOURS

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COMPACT table_name USING BIN_PACK

Question 13

When creating a Delta table using the 'CREATE TABLE' statement in Spark SQL, which THREE of the following features are supported natively?

Accepted Answer

PARTITIONED BY clause. Delta Lake provides robust data management capabilities that extend standard Spark SQL. Understanding these native features is essential for building reliable, production-grade pipelines. Features like partitioning, comment support, and table properties are standard, while advanced features like generated columns improve data quality. Knowing what is supported ensures developers can build schema-enforced, high-performance tables without needing complex workarounds or third-party libraries in their SQL code.

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AUTO_INCREMENT for primary keys

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FOREIGN KEY constraints

Question 14

You are performing a join between a large table and a table with high cardinality. Which technique should you use to avoid memory pressure on a single node during the join?

Accepted Answer

Add a salt column to the join keys.. High-cardinality join keys often lead to data skew, where a single task ends up processing a massive amount of data while others remain idle. This creates performance bottlenecks and potential OOM errors. By using techniques like salting or ensuring optimal partitioning, developers can distribute the workload evenly across the cluster, which is a vital skill for scaling Spark applications in production.

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Broadcast the large table to all executors.

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Use the COLLECT_LIST function on the join keys.

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Set the spark.sql.autoBroadcastJoinThreshold to -1.

Question 15

Which TWO of the following are benefits of using the Delta Lake format over Parquet for Spark SQL operations?

Accepted Answer

ACID transaction support. Delta Lake adds a transactional layer on top of Parquet, providing ACID guarantees and time-travel capabilities that standard Parquet files lack. These features are essential for modern data lakehouses. By managing transaction logs, Delta Lake ensures that concurrent read/write operations remain consistent, which is a major pain point when using raw Parquet files without a dedicated concurrency management system.

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Automatically converts all files to CSV format.

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No need for a Hive Metastore.

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Instant query execution without any latency.