Performance in Data Lake — Learn free

Self-check

Questions 1–10 of 10

1. What does performance in a data lake refer to?
Question 1A. The efficiency with which data is stored, retrieved and processedB. The number of users who have accountsC. The size of the data lake in terabytesD. The cost of the cloud subscriptionE. The number of dashboards built on the lake
2. Why are storage formats such as Parquet or ORC recommended for data lake performance?
Question 2A. They store data as plain text for easy readingB. They store data in columns, allowing better compression and faster retrieval on large datasetsC. They require no metadataD. They are the only formats supported by cloud storageE. They automatically encrypt all data
3. How does data partitioning improve data lake performance?
Question 3A. By duplicating every dataset across regionsB. By converting all data to a single fileC. By dividing large datasets into smaller parts, such as by time, region or product category, so only the relevant portion is accessed during queriesD. By deleting old data automaticallyE. By encrypting each dataset separately
4. Which frameworks are named as able to accelerate query performance in a data lake through indexing-style capabilities?
Question 4A. AWS S3 and Azure Data LakeB. Apache Hive and AWS GlueC. Presto and AthenaD. Apache Hudi and Delta LakeE. Flink and Databricks
5. Which tools are listed for metadata management in a data lake?
Question 5A. Parquet, ORC and AvroB. Spark, Flink and PrestoC. S3, Azure Data Lake and Google Cloud StorageD. Hudi and Delta LakeE. Apache Hive, AWS Glue and Databricks Unity Catalog
6. How does caching help data lake performance?
Question 6A. Frequently accessed data is stored in cache, significantly reducing retrieval times for repeated queriesB. It permanently replaces the underlying storageC. It compresses data into columnsD. It partitions data by regionE. It enforces access policies
7. How do query engines such as Apache Spark, Presto or AWS Athena improve performance on large datasets?
Question 7A. By storing all data in memory permanentlyB. By leveraging distributed computing to run queries in parallelC. By removing the need for partitioningD. By converting SQL into spreadsheetsE. By limiting each query to one row
8. What is said about the relationship between data governance and security and data lake performance?
Question 8A. They are the single biggest driver of query speedB. They always slow the data lake downC. They do not directly impact performance, but clear access policies and encryption can prevent bottlenecks and sustain operational efficiencyD. They are irrelevant to a data lakeE. They replace the need for metadata management
9. To maintain performance as a data lake scales on platforms such as AWS S3 or Google Cloud Storage, what must scale alongside the data?
Question 9A. The number of dashboardsB. The number of data stewardsC. The size of the metadata catalogue onlyD. Compute resourcesE. The number of file formats used
10. How does maintaining data quality contribute to data lake performance?
Question 10A. It increases the amount of data storedB. It removes the need for a processing frameworkC. It makes caching unnecessaryD. It requires more transformations to be runE. Clean, high-quality data reduces the need for time-consuming transformations or error handling, speeding up processing