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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Architecture | 30% | - Data sharing architecture design - Data modeling and use cases - Development lifecycle and workload support - Object hierarchy and architectural impact - Data protection and recovery strategies |
| Topic 2: Performance Optimization | 20% | - Cost optimization and resource management - Query performance tuning - Clustering and partitioning strategies - Virtual warehouse configuration and sizing |
| Topic 3: Accounts and Security | 25% | - Account and database strategy design - Security principles and implementation scenarios - Security, compliance and governance architecture |
| Topic 4: Data Engineering | 25% | - Transformation and processing frameworks - Data loading and unloading solutions - Data pipeline and integration design |
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NEW QUESTION # 213
A stream stores data with the same columns as the source data but with additional columns.
What are those additional columns?
Answer: B,C,D
NEW QUESTION # 214
A Snowflake Architect is setting up database replication to support a disaster recovery plan. The primary database has external tables.
How should the database be replicated?
Answer: B
Explanation:
Database replication is a feature that allows you to create a copy of a database in another account, region, or cloud platform for disaster recovery or business continuity purposes. However, not all database objects can be replicated. External tables are one of the exceptions, as they reference data files stored in an external stage that is not part of Snowflake. Therefore, to replicate a database that contains external tables, you need to move the external tables to a separate database that is not replicated, and then replicate the primary database that contains the other objects. This way, you can avoid replication errors and ensure consistency between the primary and secondary databases. The other options are incorrect because they either do not address the issue of external tables, or they use an alternative method that is not supported by Snowflake. You cannot create a clone of the primary database and then replicate it, as replication only works on the original database, not on its clones. You also cannot share the primary database with another account, as sharing is a different feature that does not create a copy of the database, but rather grants access to the shared objects. Finally, you do not need to ensure that the replicated database is in the same region as the external tables, as external tables can access data files stored in any region or cloud platform, as long as the stage URL is valid and accessible. References:
* [Replication and Failover/Failback] 1
* [Introduction to External Tables] 2
* [Working with External Tables] 3
* [Replication : How to migrate an account from One Cloud Platform or Region to another in
* Snowflake] 4
NEW QUESTION # 215
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)
Answer: D,E
Explanation:
Explanation
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation
NEW QUESTION # 216
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?
Answer: A
Explanation:
The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When theAVG_QUEUED_LOADis greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.
NEW QUESTION # 217
Running EXPLAIN on a query does not require a running warehouse
Answer: B
NEW QUESTION # 218
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