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Online Test
1). What is AWS Athena??
A) A serverless interactive query service that allows you to analyze data directly from Amazon S3 using standard SQL.
B) A managed database service for storing and retrieving data.
C) A data warehousing service for large-scale data processing.
D) A data lake formation service for organizing data in S3.
2). How does Athena interact with data stored in S3??
A) Athena copies data from S3 to its own storage for querying.
B) Athena directly queries data in S3 without copying it.
C) Athena creates a local copy of the data for querying.
D) Athena uses a data warehouse to store S3 data for querying.
3). What types of data formats can Athena query??
A) CSV, JSON, Parquet, ORC, Avro.
B) Only CSV and JSON.
C) Only Parquet and ORC.
D) Only CSV.
4). How is Athena priced??
A) Per hour of usage.
B) Per query run.
C) Per gigabyte of data scanned.
D) Per number of users.
5). What is a partition in Athena??
A) A logical grouping of data based on a specific column.
B) A physical division of data within a table.
C) A type of query optimization technique.
D) A security mechanism for data access.
6). How can you improve Athena query performance??
A) By using partitions.
B) By optimizing data format (e.g., Parquet).
C) By creating indexes.
D) All of the above.
7). What is the role of AWS Glue with Athena??
A) Glue creates tables in Athena.
B) Glue optimizes Athena queries.
C) Glue extracts, transforms, and loads data into S3 for Athena to query.
D) Glue is not related to Athena.
8). Can Athena be used for real-time analytics??
A) Yes, it is optimized for real-time analytics.
B) No, it is designed for batch processing.
C) It can be used for some real-time use cases, but not optimal.
D) Athena is not relevant for real-time analytics.
9). How does Athena handle data security and access control??
A) Athena has its own security mechanisms.
B) It inherits security settings from S3.
C) Athena encrypts all data by default.
D) There is no security control in Athena.
10). What is the maximum query result size in Athena??
A) 1 GB.
B) 10 GB.
C) There is no limit.
D) Depends on the data size.
11). Can Athena be used for ad-hoc queries??
A) Yes, it is designed for ad-hoc queries.
B) No, it is only for pre-defined queries.
C) It can be used for ad-hoc queries with limitations.
D) Athena is not relevant for ad-hoc queries.
12). How does Athena handle data partitioning??
A) Athena automatically partitions data based on file names.
B) Users need to manually create partitions.
C) Athena does not support partitioning.
D) Partitioning is handled by S3.
13). What is the difference between Athena and Amazon Redshift??
A) Athena is serverless, while Redshift is a managed data warehouse.
B) Athena is for ad-hoc queries, while Redshift is for complex analytics.
C) Athena is for data lakes, while Redshift is for data warehouses.
D) All of the above.
14). Can Athena be used for machine learning workloads??
A) Yes, directly for model training and inference.
B) No, it is not suitable for machine learning.
C) Can be used for data preparation and feature engineering.
D) Athena is not relevant for machine learning.
15). How can you optimize Athena query performance??
A) By using partitions.
B) By compressing data.
C) By using projections.
D) All of the above.
16). What is the role of CTAS (Create Table As Select) in Athena??
A) To create a new table based on the results of a query.
B) To optimize existing tables for query performance.
C) To partition an existing table.
D) To create a materialized view.
17). How can you optimize Athena query performance for large datasets??
A) By increasing the number of concurrent workers.
B) By creating custom partitions based on data distribution.
C) By using compressed file formats like Parquet or ORC.
D) All of the above.
18). What is the concept of partition pruning in Athena??
A) Eliminating unnecessary data from query processing.
B) Optimizing data distribution across S3.
C) Creating indexes on partitioned columns.
D) Compressing data to reduce query time.
19). How can you handle schema evolution in Athena??
A) By creating a new table with the updated schema.
B) By using ALTER TABLE statement to modify schema.
C) By recomputing all data.
D) Athena does not support schema evolution.
20). What are the key factors to consider when choosing between Athena and Amazon Redshift for a specific use case??
A) Query latency, data volume, concurrency, and cost.
B) Data freshness, query complexity, and integration with other AWS services.
C) Scalability, elasticity, and security requirements.
D) All of the above.
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