FREE PDF NEWEST SNOWFLAKE - DEA-C02 - SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) EXAMCOLLECTION QUESTIONS ANSWERS

Free PDF Newest Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) Examcollection Questions Answers

Free PDF Newest Snowflake - DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) Examcollection Questions Answers

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

  • A. Dynamic Data Masking policies can reference external tables directly without requiring special grants.
  • 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. Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
  • 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 # 20
Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

  • A. Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
  • B. Create a temporary table with average salaries per department, then join it back to the original DataFrame.
  • C. Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
  • D. Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
  • E. Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.

Answer: D

Explanation:
Using the 'window' function (Option C) is the most efficient. Window functions are specifically designed for this type of calculation, allowing you to perform aggregations over a subset of rows related to the current row (in this case, employees in the same department) without the overhead of joins or subqueries. Option A, B and E are less efficient due to join and subquery overhead. UDFs are also typically slower than built-in functions.


NEW QUESTION # 21
A data engineer is facing performance issues with a complex analytical query in Snowflake. The query joins several large tables and uses multiple window functions. The query profile indicates that a significant amount of time is spent in the 'Remote Spill' stage. This means the data from one of the query stages is spilling to the remote disk. What are the possible root causes for 'Remote Spill' and what steps can be taken to mitigate this issue? Select two options.

  • A. The 'Remote Spill' indicates network latency issues between compute nodes. There is nothing the data engineer can do to fix this; it is an infrastructure issue.
  • B. The window functions are operating on large partitions of data, exceeding the available memory on the compute nodes. Try to reduce the partition size by pre- aggregating the data or using filtering before applying the window functions.
  • C. The virtual warehouse is not appropriately sized for the volume of data and complexity of the query. Increasing the virtual warehouse size might provide sufficient memory to avoid spilling.
  • D. The query is using a non-optimal join strategy. Review the query profile and consider using join hints to force a different join order or algorithm.
  • E. The data being queried is stored in a non-Snowflake database, making it difficult to optimize the join.

Answer: B,C

Explanation:
Options A and D are the correct root causes and solutions for remote spill. Remote spill indicates that a query stage is exceeding the available memory on the compute node, and the overflow data is written to remote storage (usually S3 for Snowflake). Option A addresses this by increasing the warehouse size. Option D addresses the root cause by suggesting a reduction in the amount of data being processed by the window functions. Option B could help but does not directly address the spill. Option C is unlikely as Snowflake manages the network internally, and while network issues could exist, it's not the first thing to troubleshoot. Option E is unlikely since Snowflake only queries data within Snowflake itself, unless its connecting to external tables.


NEW QUESTION # 22
You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

  • A. The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
  • B. Without masking poliy code, it's impossible to determine if there is anything wrong.
  • C. The WITH clause is unneccessary.
  • D. There is no code provided, so there is nothing wrong with it.
  • E. The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.

Answer: B

Explanation:
Without the masking policy code, it's impossible to determine if there are any errors. Option A is wrong without any data, Option B can be correct but we cannot know without code. Option C may be right but we do not know as well. Option D assumes there is no code provided, but we simply can't see it here. The correct answer is E, since we cannot determine the answer without code.


NEW QUESTION # 23
You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

  • A. Pre-process the log data to convert all formats into a standardized JSON format before ingestion.
  • B. Ingest all log data into a single VARIANT column in a Snowflake table.
  • C. Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required
  • D. Use Snowflake's external functions to parse the log data during query execution.
  • E. Create separate tables for each log data format (text, JSON, XML).

Answer: B,D

Explanation:
Options A and D are the most effective. Storing all log data in a VARIANT column allows for flexibility in handling different formats. Using external functions during query execution allows for on-demand parsing and transformation, avoiding the need to pre-process or create multiple tables. Option B could be viable, but introduces overhead. Option C requires creating a lot of tables. Option E is more complex than it should be for generic use cases. Parsing during query execution with Snowflake's native features/External Functions in conjunction with variant is generally recommended.


NEW QUESTION # 24
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