# Databricks Interview Guide 2026: Spark Internals, Delta Lake, and Lakehouse Architecture

Source: https://www.techinterview.org/post/3233460280/databricks-interview-guide-2026-spark-internals-delta-lake-and-lakehouse-architecture/
Updated: 2026-04-18 · techinterview.org

## Databricks Interview Guide 2026: Data Engineering, Spark Internals, and Lakehouse Architecture

Databricks built the Data + AI platform that Fortune 500 companies use to run Spark, Delta Lake, and MLflow at enterprise scale. They also created Dolly and contribute heavily to open source LLMs. Interviewing at Databricks means demonstrating deep data engineering expertise, distributed systems knowledge, and increasingly, ML systems experience.

## The Databricks Interview Process

- **Recruiter screen** (30 min) — background, role alignment

- **Technical screen** (1 hour) — coding + data engineering discussion

- **Onsite** (4–5 rounds):

  2× coding (algorithms, SQL, distributed systems problems)

  - 1× system design (data pipeline, Spark optimization, or lakehouse design)

  - 1× technical depth (Spark internals, Delta Lake ACID, or ML systems)

  - 1× behavioral

Databricks hires for both SWE and MLE roles. SWE interviews weight distributed systems and data structures; MLE interviews add ML framework depth (PyTorch, TensorFlow, MLflow).

## Core Algorithms: Data Processing Patterns

### External Sort (Merge Sort for Datasets Larger than Memory)


```
import heapq
from typing import List, Iterator
import io

def external_sort(input_data: List[int], memory_limit: int) -> List[int]:
    """
    Sort a dataset too large to fit in memory.
    This is exactly how Spark's sort-based shuffle works.

    Algorithm:
    1. Read data in chunks of memory_limit
    2. Sort each chunk in memory
    3. Write sorted chunks to disk (simulated here as lists)
    4. K-way merge the sorted chunks

    Time: O(N log N) total; O(M log M) per chunk where M=memory_limit
    Space: O(M + K) where K=number of chunks

    Real Spark: UnsafeShuffleWriter, TimSort, off-heap memory
    """
    # Phase 1: Create sorted runs
    runs = []
    for i in range(0, len(input_data), memory_limit):
        chunk = sorted(input_data[i:i + memory_limit])
        runs.append(chunk)

    # Phase 2: K-way merge using min-heap
    # Heap entries: (value, run_index, position_in_run)
    heap = []
    iterators = [iter(run) for run in runs]

    for i, it in enumerate(iterators):
        try:
            val = next(it)
            heapq.heappush(heap, (val, i))
        except StopIteration:
            pass

    result = []
    run_iters = [iter(run) for run in runs]
    positions = [0] * len(runs)

    # Rebuild with position tracking
    heap = []
    for i, run in enumerate(runs):
        if run:
            heapq.heappush(heap, (run[0], i, 0))

    while heap:
        val, run_idx, pos = heapq.heappop(heap)
        result.append(val)

        next_pos = pos + 1
        if next_pos < len(runs[run_idx]):
            heapq.heappush(heap, (runs[run_idx][next_pos], run_idx, next_pos))

    return result
```


### Delta Lake: ACID Transactions with Transaction Log


```
import json
import time
from typing import Any, Dict, List, Optional

class DeltaLakeSimulator:
    """
    Simplified Delta Lake transaction log implementation.

    Delta Lake achieves ACID on object storage (S3, ADLS) by:
    1. Transaction log: append-only JSON log of operations
    2. Optimistic concurrency control: read current version,
       write new log entry, fail if version changed
    3. Time travel: any past version accessible via log replay

    This is what makes Delta Lake different from raw Parquet on S3.
    """

    def __init__(self):
        self.transaction_log = []  # list of {version, operation, timestamp, files}
        self.data_files = {}  # filename -> [records]
        self.version = 0

    def write(self, records: List[Dict], mode: str = 'append') -> int:
        """
        Write records with ACID guarantees.
        mode: 'append' | 'overwrite'

        Returns new version number.
        """
        current_version = self.version

        # Simulate writing data file
        filename = f"part-{current_version:05d}-{int(time.time())}.parquet"
        self.data_files[filename] = records

        # Log entry
        log_entry = {
            'version': current_version + 1,
            'timestamp': time.time(),
            'operation': 'WRITE',
            'mode': mode,
            'files_added': [filename],
            'files_removed': [],
            'num_records': len(records),
        }

        if mode == 'overwrite':
            # Mark all current files as removed
            active_files = self._get_active_files(current_version)
            log_entry['files_removed'] = active_files

        self.transaction_log.append(log_entry)
        self.version += 1
        return self.version

    def read(self, version: Optional[int] = None) -> List[Dict]:
        """
        Read table at specified version (time travel).
        If version=None, reads current (latest) version.

        This is Delta Lake's key feature: time travel for auditing,
        rollback, and reproducible ML experiments.
        """
        target_version = version or self.version

        active_files = self._get_active_files(target_version)
        records = []
        for fname in active_files:
            if fname in self.data_files:
                records.extend(self.data_files[fname])
        return records

    def _get_active_files(self, at_version: int) -> List[str]:
        """Replay log to determine active files at given version."""
        added = set()
        removed = set()

        for entry in self.transaction_log:
            if entry['version'] > at_version:
                break
            if entry['mode'] == 'overwrite':
                added.clear()
                removed.update(entry['files_removed'])
            added.update(entry['files_added'])
            removed.update(entry['files_removed'])

        return list(added - removed)

    def optimize(self) -> dict:
        """
        OPTIMIZE: compact many small files into fewer large files.
        Databricks-specific feature for improving query performance.

        Small files problem: 1M Parquet files of 1MB each =
        1M file listing API calls, each adds metadata overhead.
        """
        active_files = self._get_active_files(self.version)

        if len(active_files) <= 1:
            return {'files_compacted': 0}

        # Read all data
        all_records = []
        for fname in active_files:
            all_records.extend(self.data_files.get(fname, []))

        # Write as single optimized file
        opt_filename = f"part-optimized-{self.version:05d}.parquet"
        self.data_files[opt_filename] = all_records

        log_entry = {
            'version': self.version + 1,
            'timestamp': time.time(),
            'operation': 'OPTIMIZE',
            'mode': 'append',
            'files_added': [opt_filename],
            'files_removed': active_files,
            'num_records': len(all_records),
        }
        self.transaction_log.append(log_entry)
        self.version += 1

        return {'files_compacted': len(active_files), 'into': 1}
```


## System Design: Real-Time Data Lakehouse

Common question: *"Design a streaming analytics pipeline that can answer queries within seconds of data landing."*


```
"""
Databricks Lakehouse Architecture:

Streaming Sources          Storage Layer          Query Layer
(Kafka, Kinesis, etc.)         |                      |
        |                   [Delta Lake]          [Databricks SQL]
[Spark Structured          (Bronze/Silver/Gold)    [Apache Spark]
 Streaming]                    |                   [ML Inference]
        |                   [Unity Catalog]
[Auto Loader]              (governance, lineage)
(S3 ? Delta ingestion)

Medallion Architecture:
Bronze: Raw ingestion (immutable, schema-on-read)
Silver: Cleaned, deduplicated, joined (schema-on-write)
Gold: Aggregated business metrics (optimized for BI queries)
"""
```


## Spark Optimization Concepts

Databricks interviewers expect depth on Spark performance:

- **Data skew**: One partition has 10x data of others; fix with salting or skew join hints

- **Shuffle optimization**: Reduce shuffle with broadcast joins (small table fits in memory); use `spark.sql.autoBroadcastJoinThreshold`

- **Predicate pushdown**: Push filters down to Parquet/Delta file scanning; Delta's data skipping uses min/max stats

- **Catalyst optimizer**: Rule-based and cost-based optimization; analyze with `explain(mode='cost')`

- **AQE (Adaptive Query Execution)**: Runtime plan changes based on runtime statistics; enabled by default in Spark 3.x

## Behavioral Questions at Databricks

- **"Why Databricks over Snowflake/BigQuery?"** — Know the competitive landscape; openness, cost, ML integration

- **Customer obsession**: Databricks is customer-funded enterprise; show examples of customer-first thinking

- **Technical depth + breadth**: They want T-shaped engineers — deep in data systems, broad in ML awareness

- **Open source mindset**: Databricks contributors to Apache Spark, Delta Lake, MLflow; OSS matters here

## Compensation (L4–L6, US, 2025 data)

| Level | Title | Base | Total Comp |
| --- | --- | --- | --- |
| L4 | SWE II | $180–210K | $250–330K |
| L5 | Senior SWE | $210–250K | $330–450K |
| L6 | Staff SWE | $250–290K | $450–600K |

*Databricks is valued at ~$43B (Series I, 2023). Strong IPO candidate; equity meaningful but illiquid until public. Well-funded with strong revenue growth.*

## Interview Tips

- **Know Delta Lake**: Read the Delta Lake paper; understand transaction log, Z-ordering, VACUUM, OPTIMIZE

- **Spark internals**: DAG execution, shuffle, serialization, garbage collection tuning

- **SQL window functions**: Heavy SQL usage; `RANK()`, `LAG()`, `LEAD()`, `PARTITION BY` are tested

- **MLflow familiarity**: Even for SWE roles, knowing how experiments/runs/models work is valued

- **LeetCode focus**: Medium with emphasis on DP and sorting; data-processing patterns over pure algorithms

*Practice problems: LeetCode 295 (Find Median Data Stream), 218 (Skyline Problem), 315 (Count Smaller Numbers After Self), 327 (Count of Range Sum).*

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Related system design: [Divide and Conquer Interview Patterns: Merge Sort, Quick Select, Master Theorem](https://techinterview.org/divide-conquer-interview-patterns/)

Related system design: [System Design: Sharding and Data Partitioning Explained](https://techinterview.org/system-design-sharding-data-partitioning/)

Related system design: [System Design: API Gateway (Auth, Rate Limiting, Routing, Circuit Breaker)](https://techinterview.org/system-design-api-gateway/)

Related system design: [System Design: Apache Kafka — Architecture, Partitions, and Stream Processing](https://techinterview.org/system-design-kafka-architecture/)

Related system design: [System Design: Distributed Task Queue and Job Scheduler (Celery, SQS, Redis)](https://techinterview.org/system-design-distributed-task-queue/)

Related system design: [System Design: Collaborative Document Editing (Google Docs) — OT, CRDT, and WebSockets](https://techinterview.org/system-design-collaborative-document-editing/)

Related system design: [System Design: Distributed Counters, Leaderboards, and Real-Time Analytics](https://techinterview.org/system-design-distributed-counters-analytics/)

Related system design: [System Design: Video Processing Pipeline (YouTube/Netflix) -- Transcoding, HLS, and Scaling](https://techinterview.org/system-design-video-processing-pipeline/)

Related system design: [System Design: Event-Driven Architecture -- Kafka, Event Sourcing, CQRS, and Saga Pattern](https://techinterview.org/system-design-event-driven-architecture/)

Related system design: [System Design: Configuration Management Service -- Feature Flags, Dynamic Config, and Safe Rollouts](https://techinterview.org/system-design-config-management/)

Related system design: [System Design: Distributed Transactions -- Two-Phase Commit, Saga, and Eventual Consistency](https://techinterview.org/system-design-distributed-transaction/)

Related system design: [System Design: Distributed Locking -- Redis Redlock, ZooKeeper, and Database Locks](https://techinterview.org/system-design-distributed-locking/)

Related system design: [System Design: Log Aggregation and Observability Platform — ELK Stack, Metrics, Tracing, and Alerting](https://techinterview.org/system-design-log-aggregation-observability/)

Related system design: [System Design: Consensus Algorithms — Raft, Paxos, Leader Election, and Distributed Agreement](https://techinterview.org/system-design-consensus-algorithms-raft-paxos/)

Related system design: [System Design: Database Sharding — Horizontal Partitioning, Shard Keys, Hotspots, and Resharding](https://techinterview.org/system-design-database-sharding/)

Related system design: [System Design: Online Judge — Code Execution, Sandboxing, Test Cases, and Scalable Evaluation](https://techinterview.org/system-design-online-judge/)

Related system design: [Dynamic Programming on Graphs: Shortest Path DP, DAG DP, and Tree DP (2025)](https://techinterview.org/dynamic-programming-graphs-interview/)

Related system design: [System Design: Event Sourcing and CQRS -- Immutable Event Log, Projections, and Eventual Consistency](https://techinterview.org/system-design-event-sourcing-cqrs/)

Related system design: [Amortized Analysis Interview Patterns: Dynamic Arrays, Stack Operations, and Union-Find (2025)](https://techinterview.org/amortized-analysis-interview/)

Related system design: [System Design: ML Feature Store -- Feature Computation, Storage, Serving, and Point-in-Time Correctness](https://techinterview.org/system-design-ml-feature-store/)

Related system design: [Low-Level Design: Stock Trading Platform -- Order Book, Matching Engine, and Portfolio Management](https://techinterview.org/lld-stock-trading-platform/)

Related system design: [Minimum Spanning Tree Interview Patterns: Kruskal, Prim, and Network Design Problems (2025)](https://techinterview.org/minimum-spanning-tree-interview-patterns/)

Related system design: [Low-Level Design: Analytics Dashboard — Metrics Aggregation, Time-Series Storage, and Real-Time Charting](https://techinterview.org/lld-analytics-dashboard/)

Related system design: [Rolling Hash and Rabin-Karp: String Matching Interview Patterns (2025)](https://techinterview.org/rolling-hash-rabin-karp-interview-patterns/)

Related system design: [Low-Level Design: Job Scheduler — Cron Jobs, Distributed Task Execution, and Retry Logic](https://techinterview.org/lld-job-scheduler/)

Related system design: [System Design: Observability Platform — Logs, Metrics, Distributed Tracing, and Alerting](https://techinterview.org/system-design-observability-platform/)

Related system design: [System Design: Event Sourcing and CQRS — Append-Only Events, Projections, and Read Models](https://techinterview.org/system-design-event-sourcing/)

Related system design: [System Design: Circuit Breaker, Retry, and Bulkhead — Resilience Patterns for Microservices](https://techinterview.org/system-design-circuit-breaker/)

Related system design: [System Design: Inventory Management System — Stock Tracking, Reservations, and Reorder Automation](https://techinterview.org/system-design-inventory-management/)

Related system design: [System Design: Audit Log — Immutable Event Trail, Compliance, and Tamper Detection](https://techinterview.org/system-design-audit-log/)

Related system design: [System Design: Analytics Pipeline — Ingestion, Stream Processing, and OLAP Query Layer](https://techinterview.org/system-design-analytics-pipeline/)

Related system design: [System Design: Document Store — Schema-Flexible Storage, Indexing, and Consistency Trade-offs](https://techinterview.org/system-design-document-store/)

Related system design: [System Design: Search Ranking — Query Processing, Inverted Index, and Relevance Scoring](https://techinterview.org/system-design-search-ranking/)

Related system design: [System Design: Workflow Engine — DAG Execution, State Persistence, and Fault Tolerance](https://techinterview.org/system-design-workflow-engine/)

Related system design: [System Design: File Sharing Platform (Google Drive/Dropbox) — Storage, Sync, and Permissions](https://techinterview.org/system-design-file-sharing/)

See also: [Advanced Recursion and Backtracking Interview Patterns](https://techinterview.org/recursion-backtracking-advanced/)

See also: [Dynamic Programming on Strings](https://techinterview.org/dynamic-programming-strings/)

See also: [Segment Tree and Fenwick Tree Interview Patterns](https://techinterview.org/segment-tree-fenwick-tree/)

See also: [Sorting Algorithms for Interviews](https://techinterview.org/sorting-algorithms-interview/)

See also: [System Design: Analytics Dashboard](https://techinterview.org/system-design-analytics-dashboard/)

See also: [System Design: IoT Data Platform](https://techinterview.org/system-design-iot-data-platform/)

See also: [Dynamic Programming on Grids](https://techinterview.org/dynamic-programming-grid/)

See also: [Advanced Graph Algorithms for Interviews](https://techinterview.org/graph-algorithms-advanced/)

See also: [System Design: Vector Database](https://techinterview.org/system-design-vector-database/)

See also: [System Design: A/B Testing Platform](https://techinterview.org/system-design-ab-testing/)

See also: [System Design: ML Training and Serving Pipeline](https://techinterview.org/system-design-ml-pipeline/)

See also: [System Design: Access Control and Authorization](https://techinterview.org/system-design-access-control/)

See also: [System Design: Blockchain Explorer](https://techinterview.org/system-design-blockchain-explorer/)

See also: [System Design: Data Lake](https://techinterview.org/system-design-data-lake/)

See also: [System Design: Observability Platform](https://techinterview.org/system-design-observability/)

See also: [Union-Find Interview Patterns](https://techinterview.org/union-find-patterns/)

See also: [Low-Level Design: Smart Home System](https://techinterview.org/lld-smart-home/)

See also: [Advanced Trie Interview Patterns](https://techinterview.org/tries-advanced-patterns/)

Databricks interviews test streaming aggregations with heaps. Review patterns in [Heap and Priority Queue Interview Patterns](https://www.techinterview.org/post/3233466504/heap-patterns/).

Databricks interviews test analytics and reporting design. Review time entry reporting patterns in [Time Tracking System Low-Level Design](https://www.techinterview.org/post/3233466529/lld-time-tracking/).

Databricks interviews cover graph analytics and semantic search. Review knowledge graph design in [Knowledge Graph System Design](https://www.techinterview.org/post/3233466551/system-design-knowledge-graph/).

Databricks interviews test range aggregation data structures. Review segment tree patterns in [Segment Tree and Fenwick Tree Patterns](https://www.techinterview.org/post/3233460884/segment-tree-fenwick-tree-interview/).

Databricks stream processing powers real-time sports analytics. Review the full design in [Live Sports Score Platform System Design](https://www.techinterview.org/post/3233462676/system-design-live-sports-scores/).

Databricks interviews cover large-scale pipelines. Review video processing architecture in [Video Processing Platform System Design](https://www.techinterview.org/post/3233464658/system-design-video-processing-pipeline/).

Databricks interviews include matrix problems. Review 2D BFS, DFS, and binary search patterns in [Matrix and 2D Grid Interview Patterns](https://www.techinterview.org/post/3233466624/matrix-patterns/).

Databricks interviews cover distributed consistency. Review collaborative document design in [Collaborative Document Editing System Design](https://www.techinterview.org/post/3233466673/system-design-collaborative-doc/).

See also: [Priority Queue and Monotonic Queue Patterns: Sliding Window, Top-K, and Scheduling](https://www.techinterview.org/post/3233466720/priority-queue-patterns/)

See also: [Low-Level Design: Code Review Tool - Diffs, Comments, Approvals, and CI Integration (2025)](https://www.techinterview.org/post/3233469720/lld-code-review-system/)

## Related Interview Topics

- [Low-Level Design: Workflow Engine - DAG Execution, Step Retry, and State Persistence (2025)](https://www.techinterview.org/post/3233466769/lld-workflow-engine/)

- [System Design: A/B Testing Platform - Experiment Management, Assignment, and Statistical Analysis (2025)](https://www.techinterview.org/post/3233466239/system-design-ab-testing/)

Databricks interviews cover ML statistics. Review Bloom filters, random sampling, and weighted selection in [Probability and Statistics Interview Patterns](https://www.techinterview.org/post/3233466779/probability-statistics-interview/).

Databricks interviews include data aggregation patterns. Review BIT, segment tree, and 2D prefix sums in [Range Query Interview Patterns](https://www.techinterview.org/post/3233466803/range-query-patterns/).

Databricks tests data processing algorithms. Review sorting patterns, cyclic sort, and Dutch flag in [Sorting Algorithm Interview Patterns](https://www.techinterview.org/post/3233466899/sorting-patterns/).

Databricks interviews include recursive DP. Review tree DP patterns and the universal post-order template in [Dynamic Programming on Trees Interview Patterns](https://www.techinterview.org/post/3233465704/dp-on-trees-interview-patterns/).

Databricks builds data engineering infrastructure. Review Lambda vs Kappa, stream processing, and warehouse design in [Analytics Data Pipeline System Design](https://www.techinterview.org/post/3233466972/lld-analytics-pipeline/).

Databricks tests DP patterns. Review 2D grid DP, space optimization, and interleaving string in [2D Dynamic Programming Interview Patterns](https://www.techinterview.org/post/3233466996/dp-2d-patterns/).

Databricks includes advanced DP. Review interval DP patterns and the last-operation insight in [Dynamic Programming on Intervals Interview Patterns](https://www.techinterview.org/post/3233467020/dp-intervals-patterns/).

Databricks system design covers distributed coordination. Review fencing tokens and ZooKeeper locks in [Distributed Lock System Low-Level Design](https://www.techinterview.org/post/3233472666/lld-distributed-lock-manager/).

Databricks system design covers real-time analytics pipelines. Review the full LLD in [Real-Time Analytics Platform Low-Level Design](https://www.techinterview.org/post/3233467211/lld-realtime-analytics/).

Databricks system design covers distributed config and feature flags. Review the full LLD in [Distributed Configuration Service Low-Level Design](https://www.techinterview.org/post/3233467259/lld-config-service/).

Databricks system design covers data permissions. Review RBAC and multi-tenant access control in [Permission and Authorization System Low-Level Design](https://www.techinterview.org/post/3233467283/lld-permission-system/).

Databricks system design covers data pipelines and streaming. Review the full LLD in [Data Pipeline System Low-Level Design](https://www.techinterview.org/post/3233473007/lld-data-pipeline-architecture/).

Databricks system design covers multi-region data replication. Review the full multi-region LLD in [Multi-Region Architecture Low-Level Design](https://www.techinterview.org/post/3233467353/lld-multi-region/).

Databricks system design covers observability and log pipelines. Review the full log aggregator LLD in [Log Aggregation System Low-Level Design](https://www.techinterview.org/post/3233467403/lld-log-aggregator/).

Databricks system design covers caching layers. Review the cache system LLD in [Cache System Low-Level Design (LRU, Write Policies, Redis Cluster)](https://www.techinterview.org/post/3233467424/lld-cache-system/).

Databricks system design covers deployment automation. Review the full CI/CD pipeline LLD in [CI/CD Pipeline System Low-Level Design](https://www.techinterview.org/post/3233467426/lld-ci-cd-pipeline/).

Databricks system design covers event-driven architectures. Review the event sourcing LLD in [Event Sourcing System Low-Level Design](https://www.techinterview.org/post/3233472937/lld-event-sourcing-cqrs/).

Databricks system design covers ML feature stores. Review the online/offline store and point-in-time join LLD in [Feature Store System Low-Level Design](https://www.techinterview.org/post/3233467472/lld-feature-store/).

Multi-tenant architecture and data isolation design is in our [Multi-Tenancy System Low-Level Design](https://www.techinterview.org/post/3233473385/lld-multi-tenancy-design/).

Time-series data storage and query optimization is covered in our [Time-Series Database Low-Level Design](https://www.techinterview.org/post/3233467616/lld-time-series-db/).

Distributed config management system design is covered in our [Configuration Management System Low-Level Design](https://www.techinterview.org/post/3233472825/lld-config-management-system/).

Data masking and pseudonymization system design is covered in our [Data Masking System Low-Level Design](https://www.techinterview.org/post/3233467709/lld-data-masking/).

Large-scale data export system design is covered in our [Data Export Service Low-Level Design](https://www.techinterview.org/post/3233469726/lld-data-export-service/).

Bulk data processing system design is covered in our [Bulk Operations System Low-Level Design](https://www.techinterview.org/post/3233467780/lld-bulk-operations/).

Data archival and tiered storage design is covered in our [Archive System Low-Level Design](https://www.techinterview.org/post/3233467830/lld-archive-system/).

Time series metrics and observability design is covered in our [Time Series Metrics System Low-Level Design](https://www.techinterview.org/post/3233467934/lld-time-series-metrics/).

Database sharding and large-scale data partitioning design is covered in our [Database Sharding Low-Level Design](https://www.techinterview.org/post/3233468059/lld-database-sharding/).

Change data capture and real-time data pipeline design is covered in our [Change Data Capture (CDC) Low-Level Design](https://www.techinterview.org/post/3233468066/lld-change-data-capture/).

Consistent hashing and distributed data partitioning design is covered in our [Consistent Hashing Low-Level Design](https://www.techinterview.org/post/3233472622/lld-consistent-hashing-deep-dive/).

Bloom filter and large-scale data lookup optimization is covered in our [Bloom Filter Low-Level Design](https://www.techinterview.org/post/3233469055/lld-bloom-filter-service/).

Event replay and data pipeline recovery design is covered in our [Event Replay Low-Level Design](https://www.techinterview.org/post/3233468241/lld-event-replay/).

Configuration management and distributed config propagation design is covered in our [Configuration Management System Low-Level Design](https://www.techinterview.org/post/3233468286/lld-configuration-management/).