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| Section | Objectives |
|---|---|
| Data Loading and Unloading | - Data export
|
| Security, Governance, and Data Sharing | - Data sharing and governance
|
| Data Transformation and Analysis | - SQL-based transformations
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
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NEW QUESTION # 41
You are tasked with retrieving data from a source system that outputs a large stream of semi-structured JSON data'. The data contains nested arrays and deeply nested objects. The data needs to be transformed before being loaded into Snowflake to flatten the structure and extract relevant fields. Which approach is most efficient and scalable for retrieving and preparing this data?
Answer: A
Explanation:
Option C is the most efficient and scalable. Using an external stream processing framework allows for real-time or near real-time transformation and flattening of the JSON data before it even reaches Snowflake, offloading the processing burden. Options A and B might struggle with very large and complex JSON structures. The SnowSQL script in option A will be complex and less efficient. Transforming within a view in option B delays the transformation and can impact query performance. Option D might face performance issues as passing each record to a Python UDF can be overhead heavy. External tables (option E) are generally best for read-only access and might not be suitable for complex transformations.
NEW QUESTION # 42
You have a table 'CUSTOMER DATA with sensitive information like 'SSN' and 'CREDIT CARD NUMBER. You need to provide access to analysts for reporting purposes but must mask the sensitive columns. You decide to use a combination of Secure Views and data masking policies. Consider the following code snippets:
An analyst with the 'ANALYST ROLE executes the following query: SELECT FROM ANALYST CUSTOMER VIEW LIMIT 10; What will be the value of the 'SSN' column in the result set for the analyst?
Answer: C
Explanation:
The masking policy 'mask_ssn' is applied to the 'SSN' column in the 'CUSTOMER DATA' table. Because the analyst executing the query has the 'ANALYST_ROLE, the masking policy will transform the 'SSN' value to - before the result set is returned. Secure Views can access masked columns and display them according to the policy definition. The secure view ensures that the analyst can only access the data through the view, further enhancing security.
NEW QUESTION # 43
In Snowflake, how do window functions differ from table functions?
Answer: A
Explanation:
Table functions operate on windowed rows, distinguishing them from window functions in Snowflake.
NEW QUESTION # 44
You are tasked with cleaning a 'customer_orders' table in Snowflake. The table contains columns like 'order_id', 'customer_id', 'order_date', and 'order_amount'. You notice that some 'order_amount' values are negative (representing returns), but you need to analyze total sales. Additionally, some 'order_date' values are in the future. Which of the following SQL transformations would BEST address these data quality issues to ensure accurate sales analysis?
Answer: E
Explanation:
Option C correctly addresses both issues. ensures that all amounts are positive, effectively treating returns as positive contributions to sales for the purpose of this specific analysis. 'order_date <= filters out future dates. Options A and D also exclude negative order amounts which is incorrect, whereas options B and E do not make all amounts positive for sales amount. The use of CTE is also correct. The question is testing the candidate on how to select the best transformations for given data challenges.
NEW QUESTION # 45
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?
Answer: B
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 46
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