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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Architecture | 30% | - Storage and Compute Design
|
| Topic 2: Data Engineering | 25% | - Data Transformation
|
| Topic 3: Accounts and Security | 25% | - Access Control and Authentication
|
| Topic 4: Performance Optimization | 20% | - Cost and Resource Optimization
|
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NEW QUESTION # 201
Refreshing a secondary database is not allowed in the following circumstances
Answer: B,D
NEW QUESTION # 202
An Architect runs the following SQL query:
How can this query be interpreted?
Answer: D
Explanation:
A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.
Reference:
: Stages
: LIST Function
NEW QUESTION # 203
Why might a Snowflake Architect use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake? (Select TWO).
Answer: A,C
Explanation:
A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:
A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.
The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:
Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.
Snowflake Architect Training
Data Modeling: Understanding the Star and Snowflake Schemas
Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
Star Schema vs Snowflake Schema: 5 Key Differences
Dimensional Data Modeling - Snowflake schema
Star schema vs Snowflake Schema
NEW QUESTION # 204
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?
Answer: A
Explanation:
The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions.
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka
NEW QUESTION # 205
How can the Snowpipe REST API be used to keep a log of data load history?
Answer: A
Explanation:
* Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
* The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
* pipe: The fully-qualified name of the pipe to query.
* startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
* endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
* recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
* showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
* The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
1: Introduction to Snowpipe | Snowflake Documentation
2: loadHistoryScan | Snowflake Documentation
3: Monitoring Snowpipe Load History | Snowflake Documentation
NEW QUESTION # 206
......
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