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Snowflake DEA-C02 Examcollection Dumps & DEA-C02 Latest Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q67-Q72):

NEW QUESTION # 67
You're working on a data transformation pipeline in Snowflake. You need to create a SQL UDF called that accepts the following parameters: 'price' (NUMBER) (NUMBER) (BOOLEAN) The function should calculate the final price after applying the discount. If the customer , an additional 5% discount should be applied on top of the initial discount. Choose all the valid SQL UDF definitions that accurately implement the requirements. Only one Discount Percentage needs to apply to the base price.

A. Option A
B. Option E
C. Option D
D. Option C
E. Option B

Answer: A,B,D

Explanation:
Options A, C and E are correct. Option A correctly calculates the discount by first applying the initial discount and then the additional member discount multiplicatively. Option C adds the member discount to the base discount and then applies it, which is mathematically correct. Option E follows the same as Option A but has more explicit calculations. Option B is incorrect because the additional 5% is calculated based on original price, not already discounted price. Option D is also wrong since the addition discount is already handled as one whole number. This tests understanding of conditional logic and discount calculations within SQL UDFs, as well as multiple ways to achieve the same correct outcome.

NEW QUESTION # 68
A financial services company, 'Acme Finance', wants to share aggregated, anonymized transaction data with a research firm, 'Data Insights', through a Snowflake Data Clean Room. Acme Finance needs to ensure that Data Insights can only analyze the data using pre- defined aggregate functions and cannot access the raw, underlying transactional details. Acme Finance has already created a secure view to share the aggregated data'. Which of the following steps are necessary to grant Data Insights access to the data securely while enforcing the required restrictions?

A. Grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
B. Create a masking policy that only allows aggregate functions to be executed by Data Insights' role and apply it to the relevant columns in the underlying table. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
C. Create an external function that Data Insights can call to execute pre-approved aggregate functions on the underlying data. Grant USAGE on the function to Data Insights' role and create a secure view that uses that function.
D. Create a share object and grant USAGE privilege on the database containing the secure view to the share. Then, grant SELECT privilege on the secure view to the share. Finally, share the share with Data Insights' Snowflake account using their account identifier.
E. Create a row access policy that restricts the rows returned based on the role used by Data Insights. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.

Answer: D

Explanation:
Option B correctly outlines the process of creating a share, granting USAGE on the database and SELECT on the secure view to the share, and then sharing the share with the consumer account. This is the fundamental mechanism for secure data sharing in Snowflake. Option A is incorrect because it doesn't utilize sharing. Options C and D are data governance mechanisms (masking and row access policies) that control data visibility within an account, not across accounts. Option E suggests an external function, which is not a standard approach for Data Clean Rooms and adds unnecessary complexity.

NEW QUESTION # 69
You are developing a JavaScript UDF in Snowflake to perform complex data validation on incoming data'. The UDF needs to validate multiple fields against different criteria, including checking for null values, data type validation, and range checks. Furthermore, you need to return a JSON object containing the validation results for each field, indicating whether each field is valid or not and providing an error message if invalid. Which approach is the MOST efficient and maintainable way to structure your JavaScript UDF to achieve this?

A. Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. Return a JSON string containing the validation results.
B. Utilize a JavaScript library like Lodash or Underscore.js within the UDF to perform data manipulation and validation. Return a JSON string containing the validation results.
C. Define a JavaScript object containing validation rules and corresponding validation functions. Iterate through the object and apply the rules to the input data, collecting the validation results in a JSON object. This object is returned as a string.
D. Create separate JavaScript functions for each validation check (e.g., 'isNull', 'isValidType', 'isWithinRange'). Call these functions from the main UDF and aggregate the results into a JSON object.
E. Directly embed SQL queries within the JavaScript UDF to perform data validation checks using Snowflake's built-in functions. Return a JSON string containing the validation results.

Answer: C

Explanation:
Option D provides the most maintainable and efficient approach. By defining validation rules and corresponding functions in a JavaScript object, you can easily add, modify, or remove validation rules without affecting the core logic of the UDF. This approach promotes code reusability and makes the UDF easier to understand and maintain. Options A leads to unmaintainable code. Options B can be better than A but is still less elegant than D. Option C, while potentially useful for certain tasks, adds unnecessary overhead. Option E is generally discouraged due to performance limitations and the complexity of embedding SQL within JavaScript.

NEW QUESTION # 70
You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?

A. Create a separate transformation pipeline using Snowflake Tasks to flatten the data after it is ingested into the table.
B. Use the ' FLATTEN()' table function directly within the 'COPY INTO' statement to expand the 'items' array and extract nested fields. For nested objects, use dot notation directly in the SELECT list (e.g., 'customer.name').
C. Use JavaScript UDFs within the 'COPY INTO' statement to recursively flatten the JSON structure.
D. Pre-process the JSON documents before loading them into the stage using a custom script to flatten the structure.
E. Snowpipe and the 'COPY INTO' command automatically flattens JSON data during ingestion; no additional steps are required.

Answer: B

Explanation:
Snowflake's 'FLATTEN()' function, combined with dot notation for nested objects, provides the most efficient way to flatten JSON data during ingestion within the 'COPY INTO' statement. Options B, C, and D introduce unnecessary complexity and latency. Snowpipe does NOT automatically flatten JSON (E).

NEW QUESTION # 71
You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A. ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
B. CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
C. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;
D. ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
E. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;

Answer: D

Explanation:
The correct syntax for applying a masking policy to a column is 'ALTER TABLE ALTER COLUMN SET MASKING POLICY MODIFY COLUMN' is used for changing data types or constraints, not applying masking policies. The sign after 'SET MASKING POLICY is incorrect syntax. There's no 'APPLY MASKING POLICY command in Snowflake.

NEW QUESTION # 72
......

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