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Question 181:
A data engineer needs to implement a Delta Lake table that tracks all changes made to customer records over time. The table should maintain a complete history of updates while allowing efficient querying of the current state. Which Delta Lake feature should be used?
A) Time travel with VACUUM command
B) Change Data Feed (CDF)
C) Slowly Changing Dimension Type 2 (SCD Type 2)
D) Delta Lake transaction log
Answer: B
Explanation:
Change Data Feed (CDF) is the most appropriate Delta Lake feature for tracking all changes made to records over time while maintaining efficient query performance. CDF provides a mechanism to track row-level changes including inserts, updates, and deletes between versions of a Delta table. When enabled using ALTER TABLE table_name SET TBLPROPERTIES (delta.enableChangeDataFeed=true), CDF records change metadata that can be queried to understand what changed, when it changed, and what the previous values were. This feature is specifically designed for change tracking scenarios and provides better performance than reading full snapshots.
Option A is incorrect because time travel allows querying historical versions of tables but doesn’t specifically track individual changes, and VACUUM removes old files, which would eliminate history rather than maintain it. Option C represents a data modeling pattern rather than a Delta Lake feature. While SCD Type 2 can track changes, it requires manual implementation and doesn’t leverage Delta Lake’s built-in capabilities. Option D is incorrect because the transaction log tracks metadata about operations on the table but isn’t designed for efficient querying of row-level changes. The transaction log is used internally by Delta Lake for ACID transactions and time travel but doesn’t provide the same change tracking interface as CDF. CDF is optimized for CDC (Change Data Capture) use cases, making it ideal for maintaining complete history while allowing efficient queries of both current state and historical changes without requiring full table scans.
Question 182:
A data pipeline processes streaming data using Structured Streaming and writes to a Delta table. The pipeline experiences occasional failures due to network issues. What configuration ensures exactly-once processing semantics?
A) Setting spark.sql.streaming.checkpointLocation and using foreachBatch
B) Enabling idempotent writes with spark.databricks.delta.write.txnVersion
C) Using outputMode(“append”) with trigger(once=True)
D) Configuring maxFilesPerTrigger for rate limiting
Answer: A
Explanation:
Exactly-once processing semantics in Structured Streaming requires configuring the checkpoint location and using appropriate output sinks. The checkpointLocation stores offset information and metadata about processed records, ensuring that after a failure and restart, the streaming query resumes from the last committed offset without reprocessing or skipping data. When combined with foreachBatch, which allows custom write logic with transactional guarantees, this provides end-to-end exactly-once semantics. Delta Lake’s transactional nature ensures that writes are atomic, and the checkpoint mechanism prevents duplicate processing even across failures.
Option B is incorrect because spark.databricks.delta.write.txnVersion is not a standard configuration for ensuring exactly-once semantics in streaming. Delta Lake handles transaction versioning automatically through its transaction log. Option C is incorrect because while trigger(once=True) processes available data once and stops, and outputMode(“append”) adds new records, these alone don’t guarantee exactly-once semantics without proper checkpoint configuration. The trigger mode affects execution frequency but not processing guarantees. Option D is incorrect because maxFilesPerTrigger is a rate limiting configuration that controls how many files are processed per batch for better resource management, but it doesn’t provide exactly-once guarantees. The checkpoint location is essential for fault tolerance and exactly-once processing because it maintains state across restarts and ensures idempotent processing of streaming data.
Question 183:
A Delta table has accumulated many small files due to frequent streaming writes. Which command should be executed to improve query performance by reducing the number of files?
A) VACUUM table_name RETAIN 0 HOURS
B) OPTIMIZE table_name ZORDER BY (column_name)
C) ANALYZE TABLE table_name COMPUTE STATISTICS
D) ALTER TABLE table_name SET TBLPROPERTIES (‘delta.autoOptimize.optimizeWrite’=’true’)
Answer: B
Explanation:
The OPTIMIZE command is specifically designed to address the small file problem in Delta Lake tables by compacting small files into larger ones, significantly improving query performance. When combined with ZORDER BY, it also co-locates related data, further enhancing performance for queries that filter on the specified columns. OPTIMIZE performs bin-packing to consolidate small files into optimally-sized files based on the target file size configuration, reducing metadata overhead and improving read performance. The ZORDER BY clause uses multi-dimensional clustering to organize data within files based on specified columns, which is particularly beneficial for queries with multiple filter predicates.
Option A is incorrect because VACUUM removes old data files that are no longer referenced by the table, specifically files older than the retention period. While VACUUM cleans up storage, it doesn’t compact small files or improve query performance for current data. Setting RETAIN 0 HOURS is dangerous as it removes all historical versions immediately. Option C is incorrect because ANALYZE TABLE collects statistics about the table for the Catalyst optimizer but doesn’t physically reorganize or compact files. Statistics help with query planning but don’t solve the small file problem. Option D is incorrect because while delta.autoOptimize.optimizeWrite helps prevent small files during future writes by attempting to write optimally-sized files, it doesn’t compact existing small files. Auto-optimize is preventative, whereas OPTIMIZE is corrective. To address existing small file problems, OPTIMIZE must be run explicitly to compact and reorganize the data.
Question 184:
A data engineer needs to merge updates from a source DataFrame into a target Delta table, updating existing records and inserting new ones. Which Delta Lake operation provides this functionality?
A) INSERT OVERWRITE
B) MERGE INTO with WHEN MATCHED and WHEN NOT MATCHED clauses
C) UPDATE with a WHERE clause followed by INSERT
D) COPY INTO with merge schema option
Answer: B
Explanation:
The MERGE INTO operation (also known as upsert) is the correct Delta Lake command for simultaneously updating existing records and inserting new ones based on a join condition. MERGE INTO provides a single atomic operation that matches rows from a source table or DataFrame to a target Delta table using a merge condition. The WHEN MATCHED clause specifies actions for matching rows (typically UPDATE), while WHEN NOT MATCHED clause handles non-matching rows (typically INSERT). This operation is transactional and ensures data consistency while efficiently handling both updates and inserts in a single pass over the data.
Option A is incorrect because INSERT OVERWRITE replaces all data in the target table or partition with new data, which doesn’t preserve existing records that should remain unchanged. It’s a complete replacement operation rather than a selective update and insert. Option C is incorrect because using separate UPDATE and INSERT statements requires two operations and cannot be executed atomically. This approach is inefficient, requires multiple scans of the data, and doesn’t provide the same transactional guarantees as MERGE INTO. Additionally, ensuring correct logic to avoid duplicates becomes complex. Option D is incorrect because COPY INTO is designed for efficiently loading data from external sources into Delta tables with automatic deduplication and schema evolution, but it doesn’t support updating existing records. COPY INTO only appends new data and is optimized for incremental data ingestion from cloud storage, not for upsert operations that require matching and updating existing records.
Question 185:
A production Delta table requires point-in-time recovery capabilities. The data engineer needs to restore the table to its state from 7 days ago. Which approach accomplishes this?
A) SELECT * FROM table_name VERSION AS OF 7
B) RESTORE TABLE table_name TO TIMESTAMP AS OF ‘2024-11-11’
C) MERGE INTO table_name USING historical_backup
D) CREATE TABLE new_table SHALLOW CLONE table_name VERSION AS OF 7
Answer: B
Explanation:
The RESTORE TABLE command is specifically designed for point-in-time recovery in Delta Lake, allowing you to restore a table to an earlier version by either version number or timestamp. Using RESTORE TABLE table_name TO TIMESTAMP AS OF restores the table to its exact state at the specified timestamp, reverting all subsequent changes. This operation is metadata-only when possible and efficiently restores the table state without requiring full data copies. RESTORE is the proper command for production recovery scenarios as it directly modifies the current table to match the historical state.
Option A is incorrect because this query syntax reads data from a historical version using time travel but doesn’t restore or modify the current table state. It’s useful for querying historical data or creating reports but doesn’t perform recovery. The table remains at its current version after this query executes. Option C is incorrect because MERGE INTO is designed for upsert operations, not restoration. Using MERGE with historical data would be complex, error-prone, and wouldn’t properly handle deletions or maintain the exact historical state. It’s not designed for point-in-time recovery scenarios. Option D is incorrect because SHALLOW CLONE creates a new table that references the same data files as the source table at a specific version, but it doesn’t restore the original table. While useful for creating test environments or backup references, shallow clones don’t modify the production table. The RESTORE command directly reverts the table to the desired state, making it the appropriate choice for recovery operations.
Question 186:
A Delta table is partitioned by date column. A data engineer needs to delete all records older than 90 days while maintaining optimal performance. Which command should be used?
A) DELETE FROM table_name WHERE date < current_date() – 90
B) VACUUM table_name RETAIN 90 HOURS
C) ALTER TABLE table_name DROP PARTITION (date < ‘2024-08-20’)
D) OPTIMIZE table_name WHERE date < current_date() – 90
Answer: A
Explanation:
The DELETE FROM command with a WHERE clause is the correct approach for selectively removing records based on a condition in Delta Lake. When the table is partitioned by the date column being filtered, Delta Lake’s partition pruning automatically optimizes the operation by only scanning and modifying relevant partitions, making this operation highly efficient. The DELETE operation is transactional and maintains the Delta Lake transaction log, ensuring ACID properties are preserved. For partitioned tables, deleting old data using partition column predicates leverages metadata to quickly identify affected files without scanning the entire table.
Option B is incorrect because VACUUM removes old data files that are no longer referenced by the table’s current version, specifically files from previous versions that are older than the retention period. VACUUM doesn’t delete records from the current table version; it only cleans up files from the transaction history. The retention period in VACUUM is specified in hours and refers to how long to keep old file versions, not which records to delete. Option C is incorrect because Delta Lake doesn’t support the ALTER TABLE DROP PARTITION syntax that’s available in traditional Hive tables. Delta Lake uses a different approach to manage partitions through its transaction log rather than Hive metastore partitions. Option D is incorrect because OPTIMIZE compacts small files and optionally applies ZORDER clustering to improve read performance, but it doesn’t delete data. OPTIMIZE reorganizes existing data but maintains all records in the table, making it unsuitable for data deletion requirements.
Question 187:
A streaming pipeline writes to a Delta table and needs to prevent small files. Which Delta Lake feature automatically optimizes file sizes during writes?
A) Auto Compaction with delta.autoOptimize.autoCompact
B) Optimized Writes with delta.autoOptimize.optimizeWrite
C) Adaptive Query Execution
D) Dynamic Partition Pruning
Answer: B
Explanation:
Optimized Writes, enabled through the delta.autoOptimize.optimizeWrite table property, automatically optimizes file sizes during write operations. This feature attempts to write files that are closer to the optimal size (typically 128MB) by shuffling data before writing, reducing the small file problem that commonly occurs with streaming writes. Optimized Writes works during the write operation itself, preventing small files from being created in the first place rather than compacting them afterward. This is particularly valuable for streaming workloads where data arrives continuously in small batches.
Option A is incorrect because while Auto Compaction (delta.autoOptimize.autoCompact) does help with small files, it runs after writes are complete, automatically triggering OPTIMIZE operations on tables that have accumulated too many small files. It’s reactive rather than preventative. Auto Compaction is complementary to Optimized Writes but operates at a different stage of the process. Option C is incorrect because Adaptive Query Execution (AQE) is a Spark SQL optimization framework that dynamically adjusts query plans during execution based on runtime statistics. It improves query performance but doesn’t affect how data is written to storage or file sizes. Option D is incorrect because Dynamic Partition Pruning is a query optimization technique that reduces data scanning by eliminating partitions at runtime based on query predicates. It improves read performance but has no impact on file sizes during write operations or preventing small files in streaming scenarios.
Question 188:
A data engineer needs to create a development copy of a production Delta table without duplicating the underlying data files. Which operation achieves this?
A) CREATE TABLE dev_table DEEP CLONE prod_table
B) CREATE TABLE dev_table SHALLOW CLONE prod_table
C) CREATE TABLE dev_table AS SELECT * FROM prod_table
D) INSERT INTO dev_table SELECT * FROM prod_table
Answer: B
Explanation:
SHALLOW CLONE creates a new table that references the same underlying data files as the source table without copying the actual data, making it extremely fast and storage-efficient. The cloned table maintains its own independent transaction log, allowing it to evolve separately from the source through independent writes, updates, and deletes. Shallow clones are ideal for creating development, testing, or staging environments where you need a full copy of the table structure and data references but want to avoid storage duplication and lengthy copy operations. Changes to the cloned table don’t affect the source, and vice versa, but they initially share data files.
Option A is incorrect because DEEP CLONE creates a fully independent copy of the table by copying all data files, metadata, and transaction history. While this provides complete isolation, it requires significant time and storage space proportional to the source table size, making it unsuitable when the goal is to avoid data duplication. Deep clones are useful when complete physical separation is required. Option C is incorrect because CREATE TABLE AS SELECT (CTAS) creates a new table by reading all data from the source and writing it to new files, essentially creating a complete physical copy. This approach is time-consuming, requires double the storage, and doesn’t leverage Delta Lake’s cloning capabilities. Option D is incorrect because INSERT INTO also physically copies data by reading from the source and writing to the target table, requiring the target table to exist first and doubling storage requirements. Neither CTAS nor INSERT INTO provide the efficiency benefits of shallow cloning for creating development copies.
Question 189:
A Delta Lake pipeline needs to implement Change Data Capture (CDC) to track inserts, updates, and deletes. Which table property must be enabled?
A)logRetentionDuration
B)enableChangeDataFeed
C)deletedFileRetentionDuration
D)checkpoint.writeStatsAsJson
Answer: B
Explanation:
The delta.enableChangeDataFeed property must be set to true to enable Change Data Feed functionality in Delta Lake. Once enabled using ALTER TABLE table_name SET TBLPROPERTIES (delta.enableChangeDataFeed=true), Delta Lake begins recording row-level changes including the type of change (insert, update_preimage, update_postimage, or delete), the version when the change occurred, and the timestamp. This CDC information can be queried using the table_changes function or by reading from the _change_data folder, providing a detailed audit trail of all modifications. CDF is essential for downstream systems that need to process incremental changes rather than full table snapshots.
Option A is incorrect because delta.logRetentionDuration controls how long transaction log entries are retained before being eligible for cleanup, affecting time travel capabilities. The default is 30 days. While this affects how far back you can query historical versions, it doesn’t enable change data capture or track individual row-level changes. Option C is incorrect because delta.deletedFileRetentionDuration specifies how long data files that have been deleted or replaced should be retained before VACUUM removes them, with a default of 7 days. This property manages physical file retention for time travel but doesn’t provide CDC functionality or change tracking. Option D is incorrect because delta.checkpoint.writeStatsAsJson controls whether statistics are written in JSON format in checkpoint files for improved query planning. Checkpoints are consolidated views of the transaction log used for faster metadata access but don’t provide change data capture capabilities or track individual data modifications.
Question 190:
A data pipeline reads from a Delta table that is frequently updated. The pipeline requires consistent reads that aren’t affected by concurrent writes. Which Delta Lake isolation level guarantees this?
A) READ UNCOMMITTED
B) READ COMMITTED
C) REPEATABLE READ
D) SNAPSHOT ISOLATION
Answer: D
Explanation:
Delta Lake provides Snapshot Isolation as its default and primary isolation level, ensuring that reads always see a consistent snapshot of the table from a specific version. When a read operation begins, it reads from a specific version of the table and remains isolated from any concurrent writes or updates that occur during the read. This guarantees that queries return consistent results even when writers are actively modifying the table. Snapshot isolation prevents dirty reads, non-repeatable reads, and phantom reads while allowing high concurrency between readers and writers. This isolation level is implemented through Delta Lake’s transaction log, where each operation creates a new version, and readers can access any version without blocking writers.
Option A is incorrect because READ UNCOMMITTED is the lowest isolation level that allows dirty reads, meaning transactions can see uncommitted changes from other transactions. Delta Lake doesn’t support this isolation level as it violates ACID guarantees and would compromise data consistency. This would be unsuitable for production data pipelines requiring reliable results. Option B is incorrect because while READ COMMITTED prevents dirty reads by ensuring transactions only see committed data, it doesn’t provide the level of consistency required when a query needs to see a stable snapshot throughout its execution. READ COMMITTED allows non-repeatable reads where the same query could return different results if run multiple times during a transaction. Option C is incorrect because REPEATABLE READ is an isolation level from traditional databases that prevents non-repeatable reads but may still allow phantom reads. Delta Lake doesn’t explicitly implement REPEATABLE READ; instead, it provides the stronger guarantee of SNAPSHOT ISOLATION, which ensures complete consistency by reading from a specific table version.
Question 191:
A data engineer needs to convert an existing Parquet table to Delta format while preserving the data location. Which command accomplishes this?
A) CREATE TABLE delta_table USING DELTA LOCATION ‘/path/to/parquet’
B) CONVERT TO DELTA parquet./path/to/parquet
C) ALTER TABLE parquet_table SET TBLPROPERTIES (‘format’=’delta’)
D) COPY INTO delta_table FROM ‘/path/to/parquet’ FILEFORMAT = PARQUET
Answer: B
Explanation:
The CONVERT TO DELTA command is specifically designed to convert existing Parquet tables to Delta format in-place without moving or copying the underlying data files. The syntax CONVERT TO DELTA parquet./path/to/parquet or CONVERT TO DELTA table_name creates a Delta transaction log for the existing Parquet files, enabling Delta Lake features like ACID transactions, time travel, and schema enforcement while keeping files in their original location. For partitioned tables, you must specify partition columns using PARTITIONED BY. This conversion is efficient as it only adds Delta metadata without rewriting data files.
Option A is incorrect because this syntax creates a new Delta table definition pointing to a location but doesn’t convert existing Parquet files to Delta format. If Parquet files exist at that location, they won’t have the necessary Delta transaction log and won’t function as a proper Delta table with full ACID capabilities. Option C is incorrect because Delta format cannot be changed through ALTER TABLE TBLPROPERTIES. Table formats are fundamental characteristics that cannot be modified through property changes. This syntax would result in an error as Delta requires specific transaction log infrastructure. Option D is incorrect because COPY INTO reads data from a source location and writes it to a Delta table, creating new data files rather than converting existing ones. This approach duplicates data, requires additional storage space, and doesn’t preserve the original file location, making it inefficient compared to CONVERT TO DELTA for in-place conversion scenarios.
Question 192:
A Delta table has conflicting concurrent transactions where two writers attempt to modify the same records. How does Delta Lake handle this conflict?
A) Last writer wins automatically
B) Both transactions succeed with merged changes
C) Optimistic concurrency control with automatic retry
D) Write conflict exception requiring manual resolution
Answer: D
Explanation:
Delta Lake uses optimistic concurrency control, which means multiple transactions can proceed in parallel, but conflicts are detected at commit time. When two transactions modify the same data or metadata and attempt to commit concurrently, Delta Lake detects the conflict and throws a ConcurrentModificationException or write conflict exception for the transaction that commits second. This prevents data corruption and maintains ACID properties. The application must handle the exception and typically retries the transaction by re-reading the current table state, re-applying the transformation logic, and attempting to commit again. This approach provides high concurrency for non-conflicting operations while ensuring consistency when conflicts occur.
Option A is incorrect because Delta Lake doesn’t use a “last writer wins” strategy, which would risk data loss and violate ACID guarantees. Simply allowing the last transaction to overwrite changes would cause the first transaction’s modifications to be silently lost without any notification or error. Option B is incorrect because Delta Lake doesn’t automatically merge conflicting changes. Automatic merging would require understanding application semantics and could produce incorrect results. For example, if two transactions update different columns of the same row, automatically merging might be safe, but Delta Lake takes a conservative approach and requires explicit handling. Option C is incorrect because while optimistic concurrency control is used, Delta Lake doesn’t automatically retry failed transactions. Automatic retry is the application’s responsibility, not the storage layer’s. The application code must catch the conflict exception and implement appropriate retry logic with exponential backoff to handle transient conflicts.
Question 193:
A streaming query writes to a Delta table with a 10-second trigger interval. Which checkpoint configuration ensures fault tolerance?
A) .option(“checkpointLocation”, “/path/to/checkpoint”)
B) .option(“checkpointInterval”, “10 seconds”)
C) .option(“checkpointVersion”, “latest”)
D) .option(“checkpointMode”, “incremental”)
Answer: A
Explanation:
The checkpointLocation option is essential for fault tolerance in Structured Streaming with Delta Lake. This option specifies a directory where Spark maintains metadata about the streaming query’s progress, including offsets of processed data, query state, and commit information. When a streaming query fails and restarts, Spark uses the checkpoint to resume from the last successfully processed batch, ensuring exactly-once processing semantics without data loss or duplication. The checkpoint location must be a reliable, distributed file system path (like DBFS, S3, or ADLS) that persists across cluster restarts. Without a checkpoint location, the streaming query cannot recover its state after failures.
Option B is incorrect because checkpointInterval is not a valid Structured Streaming option. The trigger interval, which controls how frequently the streaming query processes new data, is set separately using .trigger(processingTime=”10 seconds”) or .trigger(Trigger.ProcessingTime(“10 seconds”)). Checkpoint writes happen automatically during query execution, not based on a separate interval configuration. Option C is incorrect because checkpointVersion is not a valid configuration option for Structured Streaming. Checkpoint management is handled internally by Spark, and there’s no need to specify versions manually. The checkpoint directory contains version-specific metadata that Spark manages automatically. Option D is incorrect because checkpointMode is not a recognized Structured Streaming configuration. The checkpoint mechanism is always incremental by design, storing only the information needed to track progress and state. The checkpointLocation is the only critical configuration required for establishing fault-tolerant streaming with proper recovery capabilities.
Question 194:
A data engineer needs to enforce a schema on write for a Delta table, rejecting any writes that don’t match the existing schema. Which table property provides this behavior?
A)schema.autoMerge.enabled = false
B)columnMapping.mode = ‘name’
C)minReaderVersion = 2
D)autoOptimize.optimizeWrite = true
Answer: A
Explanation:
Setting delta.schema.autoMerge.enabled to false enforces strict schema validation on write operations, ensuring that new data must exactly match the existing table schema. When this property is false (which is the default behavior), Delta Lake rejects writes that attempt to add new columns or modify the schema, protecting against unintended schema evolution. This is Delta Lake’s standard “schema on write” enforcement mechanism. If an application attempts to write data with additional columns or different data types, the operation fails with a schema mismatch exception, requiring explicit schema evolution through ALTER TABLE commands or by setting mergeSchema option to true for specific operations.
Option B is incorrect because delta.columnMapping.mode controls how columns are identified and tracked internally, with options like ‘name’ or ‘id’. Column mapping is used for advanced features like column renaming and dropping but doesn’t enforce schema validation on writes. It’s about internal schema representation rather than schema enforcement. Option C is incorrect because delta.minReaderVersion specifies the minimum Delta Lake reader version required to read the table, which is related to feature compatibility and what Delta capabilities the table uses. This property doesn’t control schema enforcement but rather determines which Delta Lake readers can access the table. Option D is incorrect because delta.autoOptimize.optimizeWrite is a performance optimization feature that automatically adjusts file sizes during write operations to prevent small files. It has no relationship to schema validation or enforcement and doesn’t affect whether writes with mismatched schemas are accepted or rejected.
Question 195:
A production Delta table requires maintaining 90 days of historical data for compliance. Which combination of table properties ensures this retention period?
A)logRetentionDuration = ’90 days’ and delta.deletedFileRetentionDuration = ’90 days’
B)checkpointInterval = 90 and delta.checkpointRetentionDuration = ’90 days’
C)dataRetentionDuration = ’90 days’ and delta.enableChangeDataFeed = true
D)vacuum.enabled = false and delta.timeTravel.enabled = true
Answer: A
Explanation:
To maintain 90 days of historical data in Delta Lake, you must configure both delta.logRetentionDuration and delta.deletedFileRetentionDuration to at least 90 days. The logRetentionDuration property controls how long transaction log entries are kept, which determines how far back time travel queries can go. The deletedFileRetentionDuration property specifies how long data files that have been logically deleted (through updates, deletes, or merges) remain physically stored before VACUUM can remove them. Both properties must be set to the same or longer retention period to ensure complete historical data access. After setting these properties, running VACUUM with a retention period of 90 days will clean up files older than 90 days while preserving newer history.
Option B is incorrect because delta.checkpointInterval controls how frequently checkpoint files are created (measured in number of transactions, not days), and delta.checkpointRetentionDuration is not a valid Delta Lake property. Checkpoints are optimizations for reading transaction logs efficiently and don’t directly control data retention. Option C is incorrect because delta.dataRetentionDuration is not a valid Delta Lake property. While delta.enableChangeDataFeed enables change tracking, it doesn’t enforce retention periods. Retention is controlled through the log and deleted file retention properties, not through a general data retention setting. Option D is incorrect because neither delta.vacuum.enabled nor delta.timeTravel.enabled are actual Delta Lake properties. VACUUM is a command that must be executed explicitly (not a property that can be disabled), and time travel is an inherent capability determined by log retention, not a property that can be toggled. Proper retention requires explicit configuration of the log and file retention durations.
Question 196:
A data pipeline performs a streaming aggregation and writes results to a Delta table. The aggregation state grows large over time. Which Structured Streaming feature manages state pruning?
A) Watermarking with withWatermark()
B) Checkpoint compression with coalesce()
C) State store optimization with spark.sql.streaming.stateStore.compression
D) Trigger mode with trigger(once=True)
Answer: A
Explanation:
Watermarking is the correct mechanism for managing state size in streaming aggregations by defining how long to retain state for late-arriving data. When you specify withWatermark() on an event-time column with a threshold (e.g., “10 minutes”), Structured Streaming automatically prunes state for groups that fall outside the watermark threshold, preventing unbounded state growth. The watermark tells the system that data more than the specified duration late will be discarded, allowing it to safely remove old aggregation state. This is essential for long-running streaming queries that perform windowed aggregations, as without watermarking, state would accumulate indefinitely, eventually causing memory issues.
Option B is incorrect because coalesce() reduces the number of partitions in a DataFrame but doesn’t manage stateful streaming state. Checkpoint compression relates to how checkpoint data is stored but doesn’t control when state is pruned from memory. Coalesce is a general DataFrame operation unrelated to streaming state management. Option C is incorrect because while spark.sql.streaming.stateStore.compression can reduce the storage footprint of state store checkpoints through compression, it doesn’t implement state pruning logic. Compression makes state storage more efficient but doesn’t determine when old state can be removed. State pruning requires semantic understanding of data recency, which watermarking provides. Option D is incorrect because trigger(once=True) causes the streaming query to process all available data once and then stop, which is useful for batch-like processing of streaming sources. However, it doesn’t address state management or pruning for long-running queries. The trigger mode affects execution frequency but not state lifecycle management in aggregations.
Question 197:
A Delta table uses partition columns that need to be changed. Which operation allows modifying partition specifications after table creation?
A) ALTER TABLE table_name ADD PARTITION (new_column)
B) Delta tables cannot change partition columns after creation
C) OPTIMIZE table_name REPARTITION BY (new_column)
D) CREATE TABLE new_table USING DELTA PARTITIONED BY (new_column) AS SELECT * FROM old_table
Answer: B
Explanation:
Delta Lake does not support modifying partition columns after a table has been created. Partitioning is a fundamental structural characteristic that affects how data is physically organized on disk, and changing it would require rewriting all existing data. Once a Delta table is created with specific partition columns, those columns remain fixed for the table’s lifetime. If different partitioning is needed, you must create a new table with the desired partition scheme and migrate the data. This limitation exists because partition columns determine the directory structure where data files are stored, and reorganizing this structure for existing data would be a complex and potentially dangerous operation.
Option A is incorrect because ALTER TABLE ADD PARTITION is a Hive command used to manually add partition metadata for specific partition values in traditional Hive tables, not to change the partition column schema. Delta Lake manages partitions automatically through its transaction log and doesn’t use this syntax. You cannot add new partition columns to the partitioning scheme. Option C is incorrect because OPTIMIZE with REPARTITION is not valid syntax. OPTIMIZE compacts files and applies ZORDER clustering but doesn’t change the table’s physical partitioning structure. Repartitioning in Spark affects in-memory DataFrame organization but doesn’t alter how a Delta table is partitioned on disk. Option D is correct as the workaround approach: creating a new table with the desired partitioning and copying data is the only way to effectively change partition columns. While this works, it confirms that partition columns cannot be modified directly, making option B the accurate description of Delta Lake’s behavior regarding partition column immutability.
Question 198:
A data engineer needs to query the transaction history of a Delta table to audit all operations performed. Which Delta Lake feature provides this capability?
A) DESCRIBE HISTORY table_name
B) SHOW PARTITIONS table_name
C) ANALYZE TABLE table_name COMPUTE STATISTICS
D) DESCRIBE EXTENDED table_name
Answer: A
Explanation:
The DESCRIBE HISTORY command provides complete transaction history for a Delta table, showing all operations performed including the operation type (WRITE, DELETE, UPDATE, MERGE, etc.), timestamp, user information, operation parameters, and version numbers. Each row in the history represents a committed transaction, and the command can show all historical versions or be limited using DESCRIBE HISTORY table_name LIMIT n. This audit trail is stored in the Delta transaction log and provides crucial information for compliance, debugging, and understanding data lineage. The history includes details like how many rows were added or removed, whether schema changed, and performance metrics for each operation.
Option B is incorrect because SHOW PARTITIONS displays the current partition structure of a table, listing all partition values that exist. This command provides information about data organization but doesn’t show transaction history, operations performed, or temporal changes. It’s a snapshot of current partitions, not a historical audit trail. Option C is incorrect because ANALYZE TABLE COMPUTE STATISTICS collects table statistics such as row counts, column statistics, and data distribution information for the query optimizer. While useful for query performance, it doesn’t provide transaction history or audit information about operations performed on the table. Option D is incorrect because DESCRIBE EXTENDED shows detailed metadata about the table including schema, location, properties, and storage information. It provides a comprehensive view of the table’s current configuration but doesn’t include historical operations or transaction audit trail. For auditing what operations were performed and when, DESCRIBE HISTORY is the appropriate command.
Question 199:
A streaming ETL pipeline needs to deduplicate records based on a key column before writing to a Delta table. Which approach ensures deduplication in streaming mode?
A) Use dropDuplicates() on the streaming DataFrame
B) Use foreachBatch with MERGE INTO for deduplication logic
C) Enable delta.autoOptimize.autoCompact
D) Use append mode with duplicate key constraint
Answer: B
Explanation:
Using foreachBatch with MERGE INTO provides robust deduplication for streaming data by allowing custom logic in each micro-batch. The foreachBatch function gives access to each micro-batch as a DataFrame, enabling you to execute MERGE INTO operations that match records based on key columns and update existing records or insert new ones. This approach handles deduplication across both the incoming batch and existing table data, ensuring no duplicates persist. The MERGE statement can include WHEN MATCHED clauses to update existing records and WHEN NOT MATCHED to insert new ones, providing complete control over deduplication logic while maintaining transactional consistency.
Option A is incorrect because while dropDuplicates() can remove duplicates within a single streaming batch, it has significant limitations in streaming contexts. It cannot deduplicate against historical data already in the target table, only within the current micro-batch. Additionally, dropDuplicates() in streaming requires watermarking and works only with append mode, making it unsuitable for comprehensive deduplication that needs to check against all existing table data. For true end-to-end deduplication, you need to compare incoming records with the entire table. Option C is incorrect because delta.autoOptimize.autoCompact automatically compacts small files after write operations to improve read performance but has no relationship to deduplication logic. Auto-compaction is a file management optimization that doesn’t prevent or remove duplicate records. Option D is incorrect because Delta Lake doesn’t support primary key or unique key constraints that would automatically reject duplicates. Append mode simply adds new records without checking for duplicates, and there’s no built-in duplicate key constraint mechanism in Delta Lake that would handle deduplication automatically.
Question 200:
A Delta table experiences frequent small updates scattered across many partitions. Which optimization technique provides the best performance improvement for subsequent reads?
A) VACUUM with short retention period
B) OPTIMIZE with ZORDER BY frequently queried columns
C) Increase number of partitions with repartition()
D) Enable delta.checkpoint.writeStatsAsStruct
Answer: B
Explanation:
Running OPTIMIZE with ZORDER BY on frequently queried columns provides the best performance improvement for tables with scattered small updates across partitions. OPTIMIZE performs two critical optimizations: first, it compacts small files created by updates into larger, optimally-sized files (typically targeting 1GB), reducing metadata overhead and improving I/O efficiency. Second, ZORDER BY applies multi-dimensional clustering that co-locates related data within files based on specified columns, dramatically reducing the amount of data scanned when filtering on those columns. For tables with frequent scattered updates, OPTIMIZE consolidates the fragmented files while ZORDER ensures data locality for common query patterns, making subsequent reads significantly faster.
Option A is incorrect because VACUUM removes old data files that are no longer referenced by the current table version, cleaning up storage space used by previous versions. While VACUUM is important for storage management, it doesn’t improve query performance for current data. Using a short retention period is actually risky as it limits time travel capabilities and could cause issues with long-running queries. VACUUM addresses storage costs, not read performance. Option C is incorrect because increasing partitions with repartition() affects in-memory DataFrame organization during processing but doesn’t change how the Delta table is physically partitioned on disk. Over-partitioning a Delta table can actually harm performance by creating too many small files and excessive metadata overhead. The goal should be consolidating small files, not creating more partitions. Option D is incorrect because delta.checkpoint.writeStatsAsStruct controls the format of statistics in checkpoint files, affecting how metadata is stored but providing minimal impact on actual query performance. While checkpoint format can slightly affect metadata reading speed, it doesn’t address the core problem of small fragmented files or improve data layout for query patterns. OPTIMIZE with ZORDER directly improves both file structure and data organization for optimal read performance.
