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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q225-Q230):NEW QUESTION # 225
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

- A. Option C
- B. Option D
- C. Option E
- D. Option A
- E. Option B
Answer: D
Explanation:
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.
NEW QUESTION # 226
A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

- A. Option C
- B. Option D
- C. Option B
- D. Option A
- E. Option E
Answer: C
Explanation:
'JSON ARRAYAGG' is the most efficient way to construct a JSON array from a result set in Snowflake. returns a Snowflake array, which is different from a JSON array. 'TO_JSON' is used to convert a Snowflake VARIANT to a JSON string, and 'ARRAY CONSTRUCT creates a Snowflake array from individual elements. Option E is close but creates an object with single key products and the value being the Array. 'JSON_ARRAYAGG' directly builds a JSON Array.
NEW QUESTION # 227
A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.
- A. It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.
- B. Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'
- C. Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
- D. Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
- E. Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
Answer: B
Explanation:
Snowflake streams can be configured to hide delete operations using the parameten Setting 'HIDE_DELETES = TRUE' will prevent delete operations from being exposed through the stream. Option A is incorrect as streams can be configured. Option B, while functional, adds an extra layer of complexity. Option D doesn't exist as a valid parameter for streams. Option E is a highly unconventional and unsupported approach.
NEW QUESTION # 228
You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?
- A. Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
- B. Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.
- C. Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.
- D. Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
- E. Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.
Answer: B,C,E
Explanation:
Options B, C and D are the most useful. Option B leverages Time Travel to compare data at a specific point. Option C creates an alert to check the difference in total amount. Option D prevents inconsistent data from ever entering the table. Option A attempts to fix an issue, rather than preventing it and a small warehouse will take too long to complete. Option E restricts table access, but does not resolve inconsistent data, which is not what the prompt requests.
NEW QUESTION # 229
You need to implement a data masking policy on the 'EMAIL' column of the 'EMPLOYEES' table. The requirement is to redact the entire email address with 'XXXXX' if the user's role is 'PUBLIC'. If the user's role is 'ANALYST', the domain part of the email should be visible, but the username should be redacted. For all other roles, the full email should be visible. Which of the following SQL statements CORRECTLY implements this masking policy?

- A. Option C
- B. Option D
- C. Option A
- D. Option E
- E. Option B
Answer: D
Explanation:
Option E correctly uses the 'REGEXP_REPLACE function with the regular expression to replace everything before the '@' symbol with 'XXXXX', thus redacting the username while preserving the domain. The other options have issues with the regex, potentially redacting the entire email in the 'ANALYST' case or not redacting the username properly. Option A's regex @ will replace everything till the last occurrence of @, which is not desired here. B and D have similar issues as A with the regex.
NEW QUESTION # 230
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