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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Accounts and Security | 25% | - Account and database strategy design - Security principles and implementation scenarios - Security, compliance and governance architecture |
| Topic 2: Data Engineering | 25% | - Data loading and unloading solutions - Transformation and processing frameworks - Data pipeline and integration design |
| Topic 3: Performance Optimization | 20% | - Cost optimization and resource management - Clustering and partitioning strategies - Virtual warehouse configuration and sizing - Query performance tuning |
| Topic 4: Snowflake Architecture | 30% | - Data protection and recovery strategies - Object hierarchy and architectural impact - Data modeling and use cases - Development lifecycle and workload support - Data sharing architecture design |
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NEW QUESTION # 113
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?
Answer: A
Explanation:
https://community.snowflake.com/s/article/COPY-INTO-table-Command-MATCH-BY-COLUMN-NAME- Copy-Option-Returns-Error-When-Loading-CSV-Data
NEW QUESTION # 114
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).
Answer: A,C
Explanation:
A: The Snowflake SQL REST API does support the use of a GET command, which can be used to retrieve the status of a previously submitted query or to fetch the results of a query once it has been executed.D. The use of a CALL command to a stored procedure is supported, which can return a result set, including a table. This allows the invocation of stored procedures within Snowflake through the SQL REST API.
NEW QUESTION # 115
What are some of the characteristics of result set caches? (Choose three.)
Answer: A,C,F
Explanation:
Explanation
Comprehensive and Detailed Explanation: According to the SnowPro Advanced: Architect documents and learning resources, some of the characteristics of result set caches are:
* Snowflake persists the data results for 24 hours. This means that the result set cache holds the results of
* every query executed in the past 24 hours, and can be reused if the same query is submitted again and the underlying data has not changed1.
* Each time persisted results for a query are used, a 24-hour retention period is reset. This means that the result set cache extends the lifetime of the results every time they are reused, up to a maximum of 31 days from the date and time that the query was first executed1.
* The retention period can be reset for a maximum of 31 days. This means that the result set cache will purge the results after 31 days, regardless of whether they are reused or not. After 31 days, the next time the query is submitted, a new result is generated and persisted1.
The other options are incorrect because they are not characteristics of result set caches. Option A is incorrect because Time Travel queries cannot be executed against the result set cache. Time Travel queries use the AS OF clause to access historical data that is stored in the storage layer, not the result set cache2. Option D is incorrect because the data stored in the result set cache does not contribute to storage costs. The result set cache is maintained by the service layer, and does not incur any additional charges1. Option F is incorrect because the result set cache is shared between warehouses. The result set cache is available across virtual warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed1. References: Using Persisted Query Results | Snowflake Documentation, Time Travel | Snowflake Documentation
NEW QUESTION # 116
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
Answer: C
Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.
Reference:
Snowflake Documentation: Materialized Views
Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
NEW QUESTION # 117
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)
Answer: A,D
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. Reference: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation
NEW QUESTION # 118
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