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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q144-Q149):

NEW QUESTION # 144
A company ingests JSON data representing customer orders into a Snowflake table named
'ORDERS'. The JSON structure varies, but all records contain a root-level 'customer id' and an array of 'items'. Some orders include promotional discounts applied to individual items. Which SQL query would efficiently extract the total discount amount for each customer, considering the presence of the 'discount' field within the 'items' array is not guaranteed for every item?

Answer: C

Explanation:
Option E is the most efficient and readable. It uses the TABLE(FLATTEN()) syntax, which is more modern. NVL handles the cases where 'discount' is missing, replacing null values with 0 before summing. The other options have issues: A doesn't correctly correlate orders and items, B will exclude items with null discount, C uses GET PATH which is less performant and the TO NUMBER will raise an error on missing values , D is overly verbose compared to NVL or IFF.


NEW QUESTION # 145
You have enabled auto-ingest using Snowpipe for a stage containing image files. You want to create a Directory Table to track the metadata of these image files (name, size, last modified time). After creating the Directory Table, you notice that it is not automatically updated when new image files are added to the stage. What steps should you take to ensure the Directory Table is automatically updated when new image files are added to the stage?

Answer: C

Explanation:
To enable automatic refreshing of a Directory Table, you must set the
'directoryTableAutorefreshEnabled' parameter to TRUE at the account level. Also, you must refresh the directory table using 'ALTER DIRECTORY TABLE REFRESH' for the setting to take effect. Option A suggests using a scheduled task, but this is not the automatic way to refresh them. Option B refers to Snowpipe, which is related to data loading, not directly to Directory Table updates. Option D is incorrect as Directory Tables can be set to refresh automatically. Option E sets properties on the stage which is necessary, but not sufficient; the account level setting is also required.


NEW QUESTION # 146
You are a data provider on the Snowflake Marketplace. You want to restrict access to specific rows in a shared table based on the consumer's account ID. Which of the following features in Snowflake can you use to accomplish this?

Answer: C

Explanation:
Row-level security policies allow you to define policies that filter rows based on user or role attributes. In this scenario, you can create a policy that checks the account ID of the consumer and filters the rows accordingly. Data masking policies obscure data, but do not filter rows.
Reader accounts don't apply row level filtering. Dynamic data masking is for obscuring, and External tables are for external data sources not native sharing filtering.


NEW QUESTION # 147
A company utilizes Snowflake's elastic compute capabilities for its daily batch processing jobs.
They have noticed that the warehouse consistently scales up to X-Large during the peak processing window (8 PM to 10 PM PST) and then scales back down. However, the cost of running the X-Large warehouse for these 2 hours is becoming significant. They are considering optimizing their compute costs. Which of the following strategies would be the MOST effective in reducing compute costs without significantly impacting the overall processing time of the batch jobs?

Answer: A,E

Explanation:
Optimizing SQL queries (Option D) is crucial for reducing compute costs. Efficient queries consume fewer resources, potentially reducing the need for a larger warehouse. Switching to Serverless tasks (Option E) is also highly effective, as Snowflake automatically manages compute resources and only charges for actual usage. Options A and B are less effective.
Reducing the warehouse size (A) will increase processing time, negating the cost savings. Auto- suspending (B) is good practice, but doesn't directly address the root cause. Prioritizing queries (C) is a good strategy but might not be as impactful as optimizing queries or using Serverless tasks.


NEW QUESTION # 148
Within a Snowflake Notebook, you have defined a Python variable named 'warehouse_size' with a value of 'X-SMALL'. You want to dynamically set the warehouse size for the Snowpark session using this variable. Which of the following code snippets correctly sets the warehouse size using Python variable substitution?

Answer: D

Explanation:
Option D, , is the correct and preferred method for changing the active warehouse within a Snowpark session. Snowpark provides a dedicated function for this purpose. Options A, B, C, and E, while they might seem to work using SQL commands, are less efficient and less Snowpark API idiomatic, or does not work as intended. In particular, options A, C, and E incur overhead of executing DDL statements and there are no direct command to execute SQL statement in session in the Snowflake.


NEW QUESTION # 149
......

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