Practice Free DAA-C01 Exam Online Questions
How does leveraging partition pruning optimize query performance in Snowflake?
- A . Limits query complexity and optimization possibilities
- B . Reduces data accessibility across multiple warehouses
- C . Increases storage requirements for optimized query access
- D . Filters unnecessary partitions during query execution
D
Explanation:
Partition pruning in Snowflake filters unnecessary partitions during query execution, enhancing query performance by minimizing the amount of data scanned.
When assessing how often data needs to be collected, what factors influence the determination of frequency? (Select all that apply)
- A . System downtime
- B . Data source availability
- C . Business cycle timing
- D . Rate of data change
C,D
Explanation:
The frequency of data collection is influenced by business cycle timing and the rate of data change to ensure up-to-date information.
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?
- A . Primary keys allow only one-to-one table joins
- B . Constraints limit table join operations to specific data types
- C . Primary keys ensure data uniqueness for table relationships
- D . Constraints restrict data access to authorized user roles
C
Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.
What is the primary benefit of using secure views in data analysis?
- A . Secure views offer enhanced data security while allowing selective data access.
- B . Secure views simplify complex data structures more effectively than materialized views.
- C . They prevent the creation of materialized views.
- D . They don’t impact data security but significantly enhance query performance.
A
Explanation:
Secure views enhance data security while allowing selective data access.
How do secure views contribute to data analysis practices in terms of access control and data security?
- A . Secure views enhance data security while allowing selective data access.
- B . They prevent the creation of materialized views.
- C . They restrict data access but don’t impact data security.
- D . Secure views limit data accessibility for improved security.
A
Explanation:
Secure views enhance data security while allowing selective data access.
When connecting BI tools to Snowflake for dashboard creation, which factors must be considered to ensure effective integration? (Select all that apply)
- A . Network latency between BI tool and Snowflake
- B . Encryption requirements for Snowflake data
- C . Compatibility of BI tool with Snowflake
- D . Snowflake account availability only
A,B,C
Explanation:
Effective integration involves considering network latency, compatibility, and encryption requirements for Snowflake data.
In performing data discovery to identify necessary elements from available datasets, what role do metadata play in this process?
- A . Metadata impacts data transformation processes.
- B . Metadata has no role in data discovery.
- C . Metadata provides insights into data structure only.
- D . Metadata helps in data lineage understanding.
D
Explanation:
Metadata aids in understanding data lineage, contributing to the identification of necessary elements from datasets.
When customizing data presentations in dashboards using filtering and editing techniques, what advantages do these methods offer? (Select all that apply)
- A . Enhanced data accuracy
- B . Limited data exploration
- C . Simplified data representation
- D . Improved data relevancy
A,D
Explanation:
Customizing through filtering and editing enhances data accuracy and relevancy in dashboards.
What actions are typically involved in working with and querying data in Snowflake? (Select all that apply)
- A . Using randomization techniques
- B . Employing time travel for data retrieval
- C . Identifying and handling data anomalies
- D . Leveraging materialized views for aggregations
A,B,C,D
Explanation:
Working with Snowflake data involves identifying anomalies, using randomization, employing time travel for historical data retrieval, and utilizing materialized views for enhanced query performance.