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1). Explain the trade-offs between Parquet, ORC, and Apache Arrow columnar formats for specific use cases.?
A) Compression algorithms, schema evolution, and query performance.
B) Integration with big data ecosystems, community support, and performance benchmarks.
C) Data types supported, null handling, and encoding efficiency.
D) All of the above.
2). How can you optimize Parquet file performance for large-scale analytics workloads on cloud platforms??
A) Partitioning, compression, and indexing strategies.
B) Storage tiering, data locality, and caching.
C) Query optimization, predicate pushdown, and column pruning.
D) All of the above.
3). Describe the challenges of using Parquet for real-time analytics and potential solutions.?
A) High latency, batch processing limitations, and schema evolution.
B) Using incremental updates, delta lakes, and change data capture.
C) Combining Parquet with streaming formats like Apache Kafka.
D) All of the above.
4). How can you ensure data consistency and integrity when working with Parquet files in distributed systems??
A) Using checksums, data validation, and error handling.
B) Implementing data versioning and auditing.
C) Leveraging ACID transactions for updates.
D) All of the above.
5). What are the best practices for handling schema evolution in Parquet files??
A) Using schema versioning and backward compatibility.
B) Planning for schema changes during data ingestion.
C) Implementing data migration strategies for schema updates.
D) All of the above.
6). Explain the concept of Parquet page layout and how it impacts read performance.?
A) Page size, dictionary encoding, and compression.
B) Data encoding, null handling, and repetition encoding.
C) Columnar vs. row-based storage, data types, and statistics.
D) All of the above.
7). How can you optimize Parquet file compression for different data types and use cases??
A) Choosing appropriate compression codecs based on data characteristics.
B) Balancing compression ratio and decompression performance.
C) Considering hardware acceleration for compression and decompression.
D) All of the above.
8). What are the challenges of using Parquet for machine learning workloads, and how can they be addressed??
A) Data format conversion, feature engineering, and model training performance.
B) Using Parquet-optimized machine learning frameworks.
C) Creating Parquet-specific feature engineering pipelines.
D) All of the above.
9). How can you integrate Parquet with cloud-native data processing and analytics services??
A) Using AWS Glue, EMR, and Athena.
B) Leveraging Azure Data Lake Storage and Azure Synapse Analytics.
C) Integrating with Google Cloud Dataflow and BigQuery.
D) All of the above.
10). Explain the concept of Parquet partitioning and its benefits for query performance.?
A) Dividing data into smaller files based on specific criteria.
B) Improving query performance by reducing data scanned.
C) Enabling parallel processing and workload optimization.
D) All of the above.
11). How can you ensure data security and privacy when using Parquet files??
A) Encryption, access controls, and data masking.
B) Data anonymization and pseudonymization.
C) Compliance with data privacy regulations.
D) All of the above.
12). What are the potential performance implications of using Parquet files for real-time analytics applications??
A) High latency due to file format overhead.
B) Difficulty in updating Parquet files in real time.
C) Need for specialized tools and frameworks.
D) All of the above.
13). How can you optimize Parquet file storage for cost-efficiency in cloud environments??
A) Using appropriate storage classes and compression codecs.
B) Implementing lifecycle management policies.
C) Optimizing file size and partitioning.
D) All of the above.
14). What are the emerging trends and challenges in Parquet file format and its ecosystem??
A) Support for new data types and complex structures.
B) Integration with machine learning and AI frameworks.
C) Balancing performance, storage efficiency, and query complexity.
D) All of the above.
15). How can you effectively troubleshoot performance issues when working with Parquet files??
A) Analyzing query execution plans and identifying bottlenecks.
B) Monitoring resource utilization and garbage collection.
C) Using profiling tools to measure query performance.
D) All of the above.
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