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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture | 30% | - Development lifecycle and workload support - Object hierarchy and architectural impact - Data modeling and use cases - Data protection and recovery strategies - Data sharing architecture design |
| Performance Optimization | 20% | - Query performance tuning - Cost optimization and resource management - Clustering and partitioning strategies - Virtual warehouse configuration and sizing |
| Accounts and Security | 25% | - Account and database strategy design - Security principles and implementation scenarios - Security, compliance and governance architecture |
| Data Engineering | 25% | - Data loading and unloading solutions - Data pipeline and integration design - Transformation and processing frameworks |
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問題 #103
An Architect is designing partitioned external tables for a Snowflake data lake. The data lake size may grow over time, and partition definitions may need to change in the future.
How can these requirements be met?
答案：B
解題說明：
Snowflake external tables support partitioning using the PARTITION BY clause, which allows architects to define logical partitions based on expressions derived from file paths or metadata. This approach is flexible and scalable, making it well-suited for data lakes that grow over time and may require changes to partition logic (Answer A).
Using PARTITION BY enables Snowflake to automatically manage partitions as new data arrives, without requiring manual intervention or table recreation. This aligns with Snowflake best practices for external table design and minimizes operational overhead.
The other options are invalid or unsupported in Snowflake. There is no partition_type = USER_SPECIFIED option, no METADATA$EXTERNAL_TABLE_PARTITION = MANUAL setting, and no ADD_PARTITION_COLUMN command for external tables. For the SnowPro Architect exam, understanding the correct and supported syntax for partitioned external tables is essential for designing maintainable data lake architectures.
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問題 #104
Which Snowflake architecture recommendation needs multiple Snowflake accounts for implementation?
答案：D
解題說明：
The Snowflake architecture recommendation that necessitates multiple Snowflake accounts for implementation is the separation of development, test, and production environments. This approach, known as Account per Tenant (APT), isolates tenants into separate Snowflake accounts, ensuring dedicated resources and security isolation12.
Reference
* Snowflake's white paper on "Design Patterns for Building Multi-Tenant Applications on Snowflake" discusses the APT model and its requirement for separate Snowflake accounts for each tenant1.
* Snowflake Documentation on Secure Data Sharing, which mentions the possibility of sharing data across multiple accounts3.
問題 #105
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
答案：A
解題說明：
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
問題 #106
A data ingestion pipeline loads data randomly throughout the day. A requirement states that data must be processed within 10 minutes of landing in Snowflake.
What is the MOST cost-effective solution?
答案：C
解題說明：
Streams and tasks are Snowflake's native mechanism for incremental data processing. A stream tracks row- level changes (inserts, updates, deletes) on a table, while tasks enable scheduled or conditional execution of SQL statements.
Using SYSTEM$STREAM_HAS_DATA allows the task to run only when new data is available, avoiding unnecessary compute usage (Answer D). This makes the solution both timely-meeting the 10-minute SLA- and cost-efficient, as compute resources are consumed only when there is work to do.
Views alone do not materialize transformations and still require compute at query time. Third-party orchestration introduces unnecessary operational overhead and cost. Running a task on a fixed schedule without checking stream data would waste compute cycles.
This pattern-streams + tasks with conditional execution-is a core SnowPro Architect design pattern for efficient, low-latency data pipelines.
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問題 #107
You have a view.
How will you list all the object references of the view?
答案：C
問題 #108
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