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When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?
A. Managing subscriptions and updates complicates dashboard usage.
B. Subscriptions and updates ensure timely information delivery.
C. They enhance dashboard usability without impacting data updates.
D. Subscriptions and updates don't impact dashboard management.
Answer: B
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 56
How does incorporating visualizations in reports and dashboards aid in presenting data for business use analyses?
A. Visualizations enhance data comprehension for effective analysis.
B. Presenting data visually doesn't impact business use analyses.
C. It limits data presentation to textual formats only.
D. Visualizations complicate data representation, hindering analysis.
Answer: A
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 57
A financial services company is migrating its transaction data from various sources, including mainframe systems, REST APIs, and flat files, into Snowflake. They want to leverage Snowflake's features for continuous data ingestion and transformation. Which combination of Snowflake features and external tools would provide the MOST scalable and efficient solution for this scenario?
A. A third-party ETL tool such as Informatica or Matillion to extract, transform, and load data into Snowflake using JDBC/ODBC connections, scheduling the jobs for regular execution.
B. Snowpipe with auto-ingest enabled for flat files, Kafka Connector for REST API data streamed into Snowflake, and a custom Python script running as a Snowflake external function to process mainframe data dumps staged in cloud storage.
C. Snowflake Tasks scheduled to periodically load data from external stages containing mainframe extracts, combined with materialized views for near real-time transformations.
D. Using external tables with direct access to the source systems, combined with stored procedures to transform the data on-read.
E. Snowpipe with REST API integration using AWS Lambda functions triggering file loads from S3, and a Snowpark pipeline to transform mainframe data after initial staging.
Answer: B,E
Explanation:
Options A and E provide scalable and efficient solutions. Snowpipe with auto-ingest handles flat files efficiently. Kafka Connector allows real-time ingestion of REST API data. AWS Lambda and Snowflake external functions offer serverless computing for transforming mainframe data dumps into Snowflake. Option B relies on scheduled tasks and materialized views, which are not ideal for high-volume, near real- time ingestion. Option C using external tables for direct access to the source system might have performance challenges for real-time analysis. Option D relies on third-party ETL tools, adding to cost and complexity, and doesn't leverage Snowflake's native capabilities effectively.
NEW QUESTION # 58
When implementing pre-math calculations (e.g., randomization, ranking, grouping) in Snowflake, how do they contribute to data analysis?
A. Ranking aids in data summarization and ordering
B. Pre-math calculations affect only specific data types
C. Grouping ensures data integrity and consistency
D. Randomization techniques introduce biases in data analysis
Answer: A
Explanation:
Pre-math calculations like ranking assist in summarizing and ordering data, enabling meaningful insights and comparisons in data analysis.
NEW QUESTION # 59
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?
A. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
B. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
C. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.
D. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
E. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
Answer: E
Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.
NEW QUESTION # 60
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