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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification |
| Exam Number: | ARA-C01 |
| Available Languages: | Japanese, English |
| Exam Duration: | 115 minutes |
| Exam Price: | $375 USD |
| Real Exam Qty: | 65 |
| Certificate Validity Period: | 2 years |
| Passing Score: | 750/1000 |
| Related Certifications: | SnowPro Core Certification |
| Exam Format: | Multiple Choice, Multiple Select |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates must hold an active SnowPro Core Certification. Snowflake recommends 2+ years of hands-on Snowflake architecture experience in production environments. |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/ |
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The SnowPro Advanced Architect Certification Exam covers a wide range of topics related to Snowflake's advanced architecture, including advanced Snowflake concepts, Snowflake security and access control, Snowflake data modeling, Snowflake data warehousing, and Snowflake performance tuning. SnowPro Advanced Architect Certification certification exam is designed to test your ability to apply this knowledge to real-world scenarios, and to demonstrate your expertise in designing and implementing Snowflake solutions that meet business needs.
NEW QUESTION # 218
Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)
Answer: A,B
Explanation:
According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.
SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.
Reference:
SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation
NEW QUESTION # 219
What are some of the characteristics of result set caches? (Choose three.)
Answer: A,D,E
Explanation:
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.
Reference: Snowflake Documentation on Result Set Caching.
NEW QUESTION # 220
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
Answer: A
Explanation:
The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:
* Recognizing Disk Spilling
* Managing the Kafka Connector
NEW QUESTION # 221
What are some of the characteristics of result set caches? (Choose three.)
Answer: A,D,E
Explanation:
In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.
References:Snowflake Documentation on Result Set Caching.
NEW QUESTION # 222
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
Answer: A
Explanation:
The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:
* Recognizing Disk Spilling
* Managing the Kafka Connector
NEW QUESTION # 223
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