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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification Exam |
| Exam Number: | ARA-C01 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | SnowPro Core Certification |
| Exam Price: | $375 USD |
| Real Exam Qty: | Approximately 65 |
| Available Languages: | English |
| Exam Format: | Multiple choice, Multiple select |
| Passing Score: | 750 (scaled score out of 1000) |
| Exam Duration: | 115 minutes |
| Recommended Training: | Snowflake Training & Certification Learning Path |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | SnowPro Core Certification is strongly recommended and often expected before attempting the Advanced Architect exam |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an industry-recognized certification that validates the advanced skills and knowledge of professionals in designing and implementing Snowflake solutions. It is a comprehensive exam that covers a wide range of topics, including Snowflake architecture, security, performance optimization, data modeling, and integration with other technologies. SnowPro Advanced Architect Certification certification is ideal for professionals who are responsible for managing Snowflake solutions for their organizations and want to demonstrate their expertise in this area.
NEW QUESTION # 223
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?
Answer: A
NEW QUESTION # 224
While loading data into a table from stage, which are the valid copyOptions
Answer: A,B,C,E,F
NEW QUESTION # 225
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
Answer: D
Explanation:
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.
References:
* Snowflake Documentation: Materialized Views
* Snowflake Learning: Materialized Views
https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/
NEW QUESTION # 226
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
Answer: D
Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
[CREATE TASK | Snowflake Documentation]
[Using Streams and Tasks | Snowflake Documentation]
[CREATE WAREHOUSE | Snowflake Documentation]
NEW QUESTION # 227
A Developer is having a performance issue with a Snowflake query. The query receives up to 10 different values for one parameter and then performs an aggregation over the majority of a fact table. It then joins against a smaller dimension table. This parameter value is selected by the different query users when they execute it during business hours. Both the fact and dimension tables are loaded with new data in an overnight import process.
On a Small or Medium-sized virtual warehouse, the query performs slowly. Performance is acceptable on a size Large or bigger warehouse. However, there is no budget to increase costs. The Developer needs a recommendation that does not increase compute costs to run this query.
What should the Architect recommend?
Answer: D
Explanation:
Enabling the search optimization service on the table can improve the performance of queries that have selective filtering criteria, which seems to be the case here. This service optimizes the execution of queries by creating a persistent data structure called a search access path, which allows some micro-partitions to be skipped during the scanning process. This can significantly speed up query performance without increasing compute costs1.
References
*Snowflake Documentation on Search Optimization Service1.
NEW QUESTION # 228
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