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NEW QUESTION # 172
A data warehouse has a role hierarchy where 'ANALYST' reports to 'DATA SCIENTIST' and
'DATA SCIENTIST' reports to 'DATA ENGINEER'. The 'ANALYST' role needs to create and use user-defined functions (UDFs) within a specific schema 'ANALYTICS. However, you want to restrict the ability of `ANALYST' to grant UDF usage privileges to other roles. You also want to ensure that if a new user is granted the 'ANALYST' role in the future, they automatically have the necessary privileges to create UDFs.
Which sequence of actions would best fulfill these requirements?
Answer: B
Explanation:
Option E is the most appropriate. It grants 'CREATE FUNCTION' on 'FUTURE FUNCTIONS' in the 'ANALYTICS' schema, meaning any new UDFs created will automatically grant the privilege.
This ensures future users with the 'ANALYST role can create UDFs. Granting USAGE' on the schema is necessary for the 'ANALYST' role to be able to access and interact with the schema, including creating UDFs. The use of 'FUTURE FUNCTIONS' grants is crucial for fulfilling the requirement of automatically granting privileges to new users. The USAGE on SCHEMA is necessary for analyst to use the database and to interact with the schema, so they can create UDFs. Option A is incorrect as it gives GRANT CREATE FUNCTION on schema, but not FUTURE functions inside that schema. Similarly Options B and D are also incorrect for this reason. Options B and D also unnecessarily grant USAGE on the schema to the DATA SCIENTIST. The data scientist already inherits privileges as part of hierarchy.
NEW QUESTION # 173
A junior data engineer is using the Snowflake web UI to create a view based on a complex query against a large dataset. They want to optimize the view for performance. Which of the following considerations and actions, when implemented directly through the Snowflake web UI and related SQL commands, will most effectively improve the performance of queries against this view?
Answer: B,C
Explanation:
Options A and B are the most effective. A materialized view pre-computes and stores the results, significantly improving performance for frequently accessed data, which can be created with
'CREATE MATERIALIZED VIEW' statement through UI . Ensuring proper clustering keys on the base tables is critical for efficient data retrieval, which improves query performance on any views built on them. Options C is incorrect, as using the 'WITH SECURE command encrypts the results, making it less performant. Options D, manual rewriting can be tedious and inefficient compared to allowing Snowflake's optimizer to handle the query. Option E, partitioning is not available with Snowflake.
NEW QUESTION # 174
A data warehouse contains a table 'orders' with columns 'order_id', 'customer_id', 'order_date' , and 'order_details' (VARIANT type containing nested JSON). You need to create a new table
'customer_orders' that aggregates the total order value per customer for orders placed in the year
2023. The order value is calculated as the sum of the 'price' field for each item in the 'items' array within the 'order_detailS JSON. Which of the following SQL statements achieves this with optimal performance, assuming a large dataset and leveraging best practices for querying VARIANT data?
Answer: C
Explanation:
Option D provides the most optimized and correct solution. Using 'CROSS APPLY FLATTEN' (or
`LATERAL FLATTEN' which is equivalent) efficiently un-nests the `items' array. Filtering by =
2023' before the 'FLATTEN' operation can significantly improve performance by reducing the number of rows processed by the FLATTEN' function. Option A is syntactically correct, however CROSS APPLY is preferrable to use for readability. Option B is incorrect. It attempts to use array slicing syntax which is not valid for summing the prices of all items within the array. Option C is incorrect. The is unnecessary as the 'item' is already a JSON object. The correct syntax is
'item.value' when using Option E is invalid syntax. The FLATTEN function is not valid inside SUM.
NEW QUESTION # 175
A Snowflake table 'ORDERS' contains order data, including a `ORDER DATE' column of type DATE. You want to load data from a CSV file into this table. The CSV file contains the order date in various formats, such as 'YYYY-MM-DD', 'MM/DD/YYYY', and 'DD.MM.YYYY'. You need to load the data into the 'ORDERS' table while consistently converting all date formats into the
'YYYY-MM-DD' format. Which COPY INTO statement snippet is most appropriate for handling the date format variations?
Answer: C
Explanation:
Option E provides the most robust solution by using 'TRY TO DATE' to attempt conversion from multiple possible date formats. If a conversion is successful, it converts DATE to VARCHAR, and if none are successful, it sets the value to NULL. The 'TO VARCHAR' function ensures that output is in YYYY-MM-DD' format. Other options may either fail or not handle different date formats correctly. The 'TRANSFORM AS SELECT clause within the "COPY INTO' statement provides flexibility in transforming the input data during the load process. Make sure that stage is set to internal stage when referencing itself.
NEW QUESTION # 176
A data analyst wants to schedule a Snowflake Notebook to automatically refresh a dashboard that relies on the results of a complex data transformation pipeline. Which of the following methods are valid ways to schedule the execution of a Snowflake Notebook? (Choose all that apply)
Answer: C,D
Explanation:
Snowflake Task with `SYSTEM$EXECUTE NOTEBOOK (A) allows scheduling via stored procedures. External orchestration tools (C) can also trigger Notebook execution via API or CLI.
Snowflake does not have a nativeUIscheduling option for notebooks (B). Converting the notebook to a script and using the OS scheduler (D) is possible but complex and less manageable. UDFs can only execute simple tasks; the whole notebook cannot be implemented through UDF's (E).
NEW QUESTION # 177
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