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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Data protection and recovery strategies - Data modeling and use cases - Development lifecycle and workload support - Data sharing architecture design - Object hierarchy and architectural impact |
| Performance Optimization | 20% | - Virtual warehouse configuration and sizing - Cost optimization and resource management - Query performance tuning - Clustering and partitioning strategies |
| Data Engineering | 25% | - Data pipeline and integration design - Transformation and processing frameworks - Data loading and unloading solutions |
| Accounts and Security | 25% | - Account and database strategy design - Security, compliance and governance architecture - Security principles and implementation scenarios |
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25. Frage
A group of order_admin users must delete records older than 5 years from an orders table without having DELETE privileges. The order_manager role has DELETE privileges.
How can this be achieved?
Antwort: A
Begründung:
Snowflake stored procedures can execute using either caller's rights or owner's rights. When a procedure runs with owner's rights, it executes using the privileges of the role that owns the procedure-not the role invoking it.
By creating a stored procedure owned by the order_manager role (which has DELETE privileges) and defining it to run with owner's rights, the Architect can encapsulate the business logic (delete records older than 5 years) securely (Answer C). Granting only USAGE on the procedure to order_admin allows them to execute the cleanup without directly holding DELETE privileges on the table.
Caller's rights would fail because order_admin lacks DELETE privileges. Snowflake does not support runtime selection of execution rights. This is a classic SnowPro Architect security pattern: least privilege access combined with controlled procedural execution.
26. Frage
What is the recommended strategy to choose the right sized warehouse to achieve best performance based on query processing?
Antwort: B
27. Frage
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)
Antwort: C,E
Begründung:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
* Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
* Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
* Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
* Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
* Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
* Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
* Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
References:
* Clustering Keys & Clustered Tables | Snowflake Documentation
* [Considerations for Choosing Clustering for a Table | Snowflake Documentation]
28. Frage
Which statement is not true about shared database?
Antwort: C
29. Frage
An Architect wants to stream website logs near real time to Snowflake using the Snowflake Connector for Kafka.
What characteristics should the Architect consider regarding the different ingestion methods? (Select TWO).
Antwort: D,E
Begründung:
When using the Snowflake Connector for Kafka, architects must understand the behavior differences between Snowpipe (file-based) and Snowpipe Streaming. Snowpipe Streaming is optimized for low-latency ingestion and works by continuously sending records directly into Snowflake-managed channels rather than staging files. One important characteristic is that Snowpipe Streaming automatically flushes buffered records at short, fixed intervals (approximately every second), ensuring near real-time data availability (Answer D).
Another key consideration is offset handling. The Snowflake Connector for Kafka is designed to tolerate Kafka offset jumps or resets, such as those caused by topic reprocessing or consumer group changes.
Snowflake can safely ingest records without corrupting state, relying on Kafka semantics and connector metadata to maintain consistency (Answer E).
Snowpipe Streaming is not always the default ingestion method; configuration determines whether file-based Snowpipe or Streaming is used. Schema detection is not supported in Snowpipe Streaming. Traditional Snowpipe does not offer lower latency than Snowpipe Streaming. For the SnowPro Architect exam, understanding ingestion latency, buffering behavior, and fault tolerance is essential when designing streaming architectures.
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30. Frage
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