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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q206-Q211):

NEW QUESTION # 206
What is the typical syntax for using the LIMIT clause to retrieve the first 10 rows of a table named employees?

Answer: A

Explanation:
TheLIMITclause is used to restrict the number of rows returned by a query. The correct syntax in Snowflake is:
SELECT * FROM employees LIMIT 10;
This retrieves the first 10 rows of the result set. LIMIT appears at the end of the SELECT statement.
Snowflake follows a SQL syntax similar to MySQL/PostgreSQL regarding LIMIT usage.
Incorrect forms include:
* Using LIMIT before SELECT
* Embedding LIMIT inside WHERE
* Reversing the order of SELECT and LIMIT
LIMIT is frequently used for sampling, record previewing, debugging, and exploratory analysis.


NEW QUESTION # 207
Which are the type of Views we have in Snowflake? (Choose any 3 options)

Answer: A,B,D

Explanation:
Snowflake supportsNon-Materialized Views(standard views),Materialized Views, andSecure Views. Non- materialized views compute results dynamically at query time. Materialized views store precomputed results, improving performance for repeated analytical queries. Secure views protect underlying query logic and metadata from being exposed, ensuring privacy when sharing datasets. The option "UnMaterialized" is not a Snowflake concept and does not exist as a view type.


NEW QUESTION # 208
Which of the following are numeric data types supported by Snowflake? (Choose any 2 options)

Answer: C,D

Explanation:
Snowflake supports several numeric data types, includingINTEGER(and its aliases INT, BIGINT, SMALLINT, TINYINT) andFLOAT(a floating-point type). INTEGER represents exact whole numbers, and FLOAT supports approximate numerical values with fractional components. BOOLEAN is a logical data type used for TRUE/FALSE values, not numeric. VARCHAR is a string data type, also not numeric. Thus, INTEGER and FLOAT are the correct numeric data types supported by Snowflake.


NEW QUESTION # 209
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: E

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 # 210
You are responsible for optimizing data loading into Snowflake from a large set of CSV files stored in Azure Blob Storage. The files contain millions of records and are updated daily You need to choose the MOST efficient method for loading this data, considering both performance and cost. Select TWO options that would best address this scenario.

Answer: A,C

Explanation:
Snowpipe with auto-ingest (A) provides continuous data loading as soon as new files are available in the stage, making it highly efficient for near real-time ingestion. It leverages serverless compute for loading, optimizing cost. Coupling it with a notification integration (D) makes the process even more event- driven and efficient, ensuring that data loading is triggered immediately upon file arrival. Options B is a batch process using scheduled tasks which is not ideal, and the other two aren't related to the data loading process that the question is asking.


NEW QUESTION # 211
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