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You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?
A. Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
B. Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
C. Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
D. Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
E. Create a secure view that joins the tables and share only the secure view using a data share.
Answer: E
Explanation:
Secure views are specifically designed for data sharing while protecting the underlying data sources. Sharing the secure view ensures that the consumer only sees the aggregated data and cannot access the underlying tables directly. Options A and D expose the underlying tables, increasing the risk of data leakage. Option C introduces latency due to the materialized view refresh. Option E adds unnecessary complexity and potential performance overhead.
NEW QUESTION # 101
You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?
A. Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
B. Avoid large or small number and stick to the limited range of input values.
C. Cast input arguments and the result to 'FLOAT within the UDF.
D. Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
E. Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
Answer: E
Explanation:
Option B is the most reliable solution. Using a dedicated arbitrary-precision arithmetic library like 'Big.js' or 'Decimal.js' allows you to perform calculations with a higher degree of accuracy, overcoming JavaScript's inherent limitations in handling very large or very small numbers. Option A might help with formatting the output, but it doesn't address the precision issue during calculation. Option C and D will not solve the problem. Option E is not practical.
NEW QUESTION # 102
Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)
A. Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
B. Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.
C. Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
D. Dynamic Data Masking policies can reference external tables directly without requiring special grants.
E. Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
Answer: B,C
Explanation:
Option A is correct because Dynamic Data Masking applies policies at query runtime based on context, while view-based security is defined when the view is created. Option B is correct because Dynamic Data Masking uses contextual functions like 'CURRENT and to tailor masking. Option C is incorrect; masking policies offer fine-grained control. Option D is incorrect; referencing external objects require appropriate grants. Option E is incorrect; While using both is possible, care must be taken to ensure that masking happens correctly.
NEW QUESTION # 103
You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?
A. Set 'snowflake.ingest.file.name' to an existing file in a stage.
B. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
C. Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
D. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
E. Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
Answer: B
Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.
NEW QUESTION # 104
You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?
A. Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
B. Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.
C. Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.
D. Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.
E. Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
Answer: B,D
Explanation:
Option B is the most flexible and robust. External functions allow leveraging powerful scripting languages (like Python) for parsing and manipulating JSON data, handling variations gracefully. Option E is similarly valid, using Java and Jackson, which gives similar control and flexibility. Option A is less desirable due to the complexity of handling different data types and missing fields directly within SQL. Option C is limited because it relies on predefined paths and doesn't easily handle variations in the JSON structure. Option D is not suitable since 'COPY INTO does not directly support URLs.
NEW QUESTION # 105
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