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Newest Official DEA-C02 Practice Test–Find Shortcut to Pass DEA-C02 Exam

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q274-Q279):

NEW QUESTION # 274
You are designing a data pipeline that uses the Snowflake SQLAPI to execute a series of complex SQL queries. These queries involve multiple joins, aggregations, and user-defined functions (UDFs). You need to ensure that the pipeline is resilient to transient network errors and can handle a large volume of concurrent requests. Which of the following strategies would you implement to enhance the reliability and performance of your pipeline?

A. Bundle all the SQL queries into a single, large SQL statement to reduce the number of API calls.
B. Implement exponential backoff and retry logic in your client application to handle transient errors when calling the SQL API.
C. Use the SQL API's asynchronous execution mode and poll for query status to handle long-running queries without blocking.
D. Increase the timeout value for the SQL API requests to accommodate potentially long-running queries.
E. Disable query result caching in Snowflake to ensure that the pipeline always retrieves the latest data.

Answer: B,C,D

Explanation:
Implementing exponential backoff and retry logic (A) addresses transient network errors. Increasing the timeout value (B) prevents premature termination of long-running queries. Using asynchronous execution (C) allows for non-blocking query execution and monitoring of query status. Bundling all queries into a single statement (D) can negatively impact performance and scalability. Disabling query result caching (E) is generally not recommended, as it can increase query execution time and resource consumption.

NEW QUESTION # 275
Your company utilizes Snowflake Streams and Tasks for continuous data ingestion and transformation. A critical task, 'TRANSFORM DATA', consumes data from a stream 'RAW DATA STREAW on table 'RAW DATA' and loads it into a reporting table 'REPORTING TABLE. You observe that 'TRANSFORM DATA is failing intermittently with a 'Stream is stale' error. What steps can you take to diagnose and resolve this issue? Choose all that apply.

A. Drop and recreate the stream and task to reset their states.
B. Modify the task definition to use the 'WHEN condition to prevent execution when the stream is empty.
C. Ensure that the ' TRANSFORM DATA' task is consuming the stream data frequently enough to prevent the stream from becoming stale.
D. Use the "AT' or 'BEFORE clause when querying the stream to explicitly specify a point in time to consume data from.
E. Increase the parameter at the database level to ensure Time Travel data is available for a longer period.

Answer: C,E

Explanation:
Option A is correct: Increasing will ensure that Time Travel has sufficient history, preventing the stream from becoming stale if the task is delayed. Option C is correct: Ensuring frequent consumption of the stream prevents it from exceeding the data retention period. Option B may prevent the task from running, but doesn't address the root cause of the staleness, option D will fix the problem. Option E is a drastic measure and should only be considered as a last resort.

NEW QUESTION # 276
You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

Answer: A

Explanation:
Option D provides the MOST comprehensive and accurate report. It directly queries the view, filtering for 'TAG_NAME = 'PII" and 'object_domain = 'COLUMN" to specifically target tags applied to columns. It selects the database, schema, table name, column name, tag name, and tag value, providing all the necessary information. Option A requires a JOIN between "snowflake.account_usage.columns' and , which is unnecessary for this use case and less efficient. option B is missing the OBJECT_DATABASE and OBJECT_SCHEMA which is needed to fully qualify the table. option C attempts to use a table function, which is unnecessary complexity and potentially less performant. Option E does not filter for column-level tags, potentially including tags applied to other object types (e.g., tables, views), leading to inaccurate results. The fully qualified name can be easily constructed from OBJECT DATABASE, OBJECT SCHEMA and OBJECT NAME.

NEW QUESTION # 277
You are tasked with creating a SQL UDF in Snowflake to mask sensitive customer data (email addresses) before it's used in a reporting dashboard. The masking should replace all characters before the '@' symbol with asterisks, preserving the domain part. For example, 'john.doe@example.com' should become ' @example.com'. Which of the following SQL UDF definitions correctly implements this masking logic, while minimizing the impact on Snowflake compute resources?

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

Answer: C

Explanation:
Option A correctly uses LPAD and SPLIT PART to replace the username portion of the email with asterisks while retaining the domain. It efficiently avoids unnecessary calculations or regular expressions. Option B is incorrect because it simply replaces everything before the @ with ' @', which is not dynamic. Option C's logic is flawed and would not mask correctly. Option D provides additional casing for when an email doesn't have '@' symbol, which is technically correct for handling unexpected input, but is not needed for the explicit requirement. Option E has incorrect syntax. This question assesses understanding of string manipulation functions and their optimal usage within a UDF.

NEW QUESTION # 278
You have a Snowflake table named 'CUSTOMER DATA that contains sensitive Personally Identifiable Information (PII). You need to grant a data analyst access to a subset of the data while masking specific columns containing PII. Which of the following Snowflake features, when used in combination, provides the MOST secure and efficient solution?

A. Grant the analyst the 'SELECT privilege on the 'CUSTOMER_DATA' table and implement row-level security based on the analyst's role.
B. Create a new table containing only the non-sensitive columns and grant SELECT privileges on this table.
C. Create a secure view on 'CUSTOMER DATA' with column masking policies and grant SELECT privileges on the secure view.
D. Create a stored procedure that retrieves the data with masking applied and grant the analyst EXECUTE privilege on the stored procedure.
E. Create a view on 'CUSTOMER_DATA' with column masking policies and grant SELECT privileges on the view.

Answer: C

Explanation:
Option D is the correct answer. Secure views, combined with column masking policies, offer the most secure and efficient approach. Secure views prevent the underlying table structure and definitions from being exposed to the user, adding an extra layer of security. Column masking policies dynamically mask the sensitive data based on the user's role or other criteria. The combination minimizes data exposure and simplifies management compared to other options. Options A and B don't fully hide table structure. Option C focuses on row level security. Option E performance will not be optimal for large datasets.

NEW QUESTION # 279
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