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NEW QUESTION # 81
What are computer language considerations when using Snowflake interfaces? (Select TWO).
Answer: A,D
Explanation:
Snowflake Notebooks support both SQL and Python as executable cell types. This means that users can create notebook cells written in Python for programmatic data processing and modeling, and in SQL for declarative querying and transformation. Because of this, the statements "Notebook cells can be written in Python" and
"Notebook cells can be written in SQL" are both correct.
Snowflake databases do not natively execute queries written in Scala; Scala is supported via Snowpark APIs for application code, not as a direct query language. Worksheets in Snowsight are primarily SQL-based and also support procedural constructs (e.g., Snowflake Scripting), so the phrase "only in SQL" is not a precise or complete characterization. Dashboards in Snowsight are created using SQL-backed visualizations and built-in UI components; JavaScript is not a supported authoring language inside the native dashboarding layer.
Therefore, options B, D, and E are not correct in this context.
NEW QUESTION # 82
A Snowflake administrator notices that a virtual warehouse named 'REPORTING WH' is frequently idle during off-peak hours. They want to optimize costs by ensuring the warehouse suspends automatically when not in use, but they also need to minimize the latency for the first query after a period of inactivity. Which combination of actions would achieve this goal?
Answer: A,C
Explanation:
Setting `AUTO SUSPEND to a low value (60 seconds) ensures the warehouse suspends quickly when idle (cost optimization). 'AUTO_RESUME = FALSE (A) would require manual warehouse resumption, increasing latency. Setting 'AUTO_RESUME' to TRUE (B, C, E) allows automatic resumption upon a new query. 'AUTO SUSPEND to a high value(C) defeats the purpose of cost optimization. Setting 'AUTO_SUSPEND' to NULL(D) prevents suspension, negating cost savings.
Reducing latency requires both automatic resumption (AUTO RESIJME = TRUE) AND minimizing the initial query scan time through proper clustering (E), in combination with AUTO RESUME and a low AUTO_SUSPEND time. Result Cache only helps on repeat queries with the same parameters.
NEW QUESTION # 83
You are tasked with loading JSON data containing customer information into Snowflake. The JSON structure is complex and varies across records. You want to optimize query performance on a frequently accessed nested field 'address.city'. Which of the following strategies would BEST improve query performance?
Answer: D
Explanation:
Flattening the JSON data during loading and creating a standard index on the 'address.city' column provides the best query performance because Snowflake can directly use the index to filter and retrieve data without having to parse the JSON structure at query time. Virtual columns can provide some performance improvement, but they are not as efficient as standard indexes.
Creating a separate table or using LATERAL FLATTEN and pivoting adds unnecessary complexity and overhead.
NEW QUESTION # 84
What is the highest level object in the Snowflake object hierarchy?
Answer: D
Explanation:
TheAccountis the top-level Snowflake container encompassing:
* All databases
* All schemas
* All compute resources (warehouses)
* All roles, users, and governance structures
All other objects exist within the Account context.
NEW QUESTION # 85
You are using the Snowsight user interface to monitor the performance of a Snowflake warehouse. You notice that the average query execution time is consistently high. Which of the following actions, performed DIRECTLY through Snowsight's monitoring features, would be MOST effective in identifying the root cause of the performance bottleneck?
Answer: C
Explanation:
Analyzing the Warehouse Load graph is the most effective method using Snowsight's monitoring features. This directly shows periods of high concurrency and queuing, indicating potential resource contention. Query History is useful, but analyzing the load graph is more targeted to warehouse performance as a starting point. Increasing warehouse size without understanding the bottleneck is premature. Downloading query execution plans is useful after identifying slow queries, and the Data Marketplace is irrelevant to performance monitoring.
NEW QUESTION # 86
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