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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q133-Q138):

NEW QUESTION # 133
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_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
D. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY 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: C

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 # 134
A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

A. Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
B. Clustering only on PRODUCT_CATEGORY.
C. Creating separate tables for each 'PRODUCT CATEGORY.
D. Clustering on followed by 'SALE_DATE'.
E. Clustering only on 'SALE DATE

Answer: A

Explanation:
Clustering on 'SALE_DATE followed by 'PRODUCT_CATEGORY is the most effective strategy. Since queries filter on 'SALE DATE and then aggregate by 'PRODUCT CATEGORY, this order ensures that micro-partitions are pruned efficiently based on date, and then within each date range, data is further organized by product category, reducing the amount of data scanned for aggregations. Options A and B only address one part of the query pattern. Option D may result in less efficient pruning on the date filter. Option E is an anti-pattern, it leads to table proliferation and maintenance overhead instead of proper clustering.

NEW QUESTION # 135
You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A. Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
B. Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
C. Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.
D. Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
E. Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.

Answer: C

Explanation:
Option C is the most efficient and self-contained approach using native Snowflake features. Embedding the retry logic within the stored procedure called by 'task_ff allows for fine-grained control over the retry process, exception handling, and delay implementation. The retry count limit prevents infinite loops. Option A, while technically feasible, involves querying the TASK HISTORY view, which can be less efficient. Option B requires creating and managing an additional task. Option D introduces external dependencies, making the solution more complex. Option E does not address the retry mechanism.

NEW QUESTION # 136
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 policies can reference external tables directly without requiring special grants.
D. Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
E. Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.

Answer: B,D

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 # 137
You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

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

Answer: C

Explanation:
Option B correctly addresses all the requirements. It uses 'product_id').cast('string')' to extract the product_id' from the JSON and cast it to a string; filters for non-null 'product_id' values and 'event_time' within the last hour using 'dateadd' and 'current_timestamp' for efficient time filtering; and then groups by 'product_id' to count the events. Option A includes 'to_timestamp' which is unneccessary. Option C extracts the 'product_id' from the JSON string to create 'product_id' without casting it to string type. This could create issues in later aggregations/filtering if the data types do not match up. Option D uses subtraction on 'current_timestamp' with the addition result of 'dateadd('hour', 1, current_timestamp()Y to perform time filtering. This will subtract a future time from the current time. This is incorrect.Option E passes a SQL string directly to the filter, bypassing Snowpark+s function calls.

NEW QUESTION # 138
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Free PDF 2025 Snowflake DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Perfect Latest Test Practice

Free PDF Quiz DEA-C02 - Fantastic SnowPro Advanced: Data Engineer (DEA-C02) Latest Test Practice

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q133-Q138):

Snowflake DEA-C02 PDF Dumps Format - Easy To Use

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