INSTANT DEA-C02 DISCOUNT - DEA-C02 RELIABLE EXAM CAMP

Instant DEA-C02 Discount - DEA-C02 Reliable Exam Camp

Instant DEA-C02 Discount - DEA-C02 Reliable Exam Camp

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q78-Q83):

NEW QUESTION # 78
You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?

  • A. Grant the 'APPLY MASKING POLICY privilege to the user running the batch job, allowing them to bypass the masking policy.
  • B.
  • C. Create a new role with 'ACCOUNTADMIN' privileges, assign this role to the batch job process during the execution, and then revert back to the original role after the job is done.
  • D. Create a temporary view on the 'EMPLOYEES' table without the 'SSN' column, grant access to the view for the batch job, and drop the view after the job is complete.
  • E. Set the 'DISABLE MASKING' session parameter to 'TRUE' for the batch job session. This will temporarily disable all masking policies.

Answer: B

Explanation:
Option E is the best approach. The function'SYSTEM$GET_PRlVlLEGE MASKING POLICY', helps bypass masking only if the user has such a privilege. Option A is incorrect because it is giving unnecessary privilege and will become permanently effective if it is not revoked. Option B, while technically feasible, is not efficient. Option C is dangerous as ACCOUNTADMIN has too many privileges. Option D disables all masking policies and does not exist in Snowflake.


NEW QUESTION # 79
You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?

  • A. Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF
  • B. Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
  • C. Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.
  • D. Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.
  • E. Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.

Answer: D

Explanation:
SQL UDFs are generally the most efficient for simple calculations within Snowflake because they are executed within the Snowflake engine, minimizing data movement and overhead. While Java UDFs (option B) can offer optimizations, the overhead of invoking the Java environment often outweighs the benefits for this type of calculation. External Functions (option C) introduce significant latency due to network communication. Option D provides temporary performance improvements for the specific session, but is not the most efficient general solution. Vectorized keyword doesn't exists in snowflake to create UDFs, Hence it won't allow compilation. This questions emphasis on understanding the trade-offs between different UDF types and their performance implications within the Snowflake architecture.


NEW QUESTION # 80
You are tasked with implementing a data governance strategy in Snowflake for a large data warehouse. Your objective is to classify sensitive data columns, such as customer phone numbers and email addresses, using tags. You want to define a flexible tagging system that allows different levels of sensitivity (e.g., 'Confidential', 'Restricted') to be applied to various columns. Furthermore, you need to ensure that any data replicated to different regions maintains these classifications. Which of the following statements accurately describe best practices for implementing and maintaining data classification using tags in Snowflake, especially in a multi-region setup? Choose TWO.

  • A. Always grant the ACCOUNTADMIN role to users who need to apply tags. This simplifies the process and ensures they have all necessary privileges.
  • B. When replicating data between regions, the tags are automatically replicated along with the data, provided that replication is configured using database replication or failover groups including the tagging schema.
  • C. Define tag schemas at the account level and replicate them to all regions. This ensures consistency of tag definitions across the entire organization.
  • D. Tags and tag values must be uniquely defined across all schemas to avoid conflicts and ensure accurate data classification; Snowflake enforces uniqueness implicitly.
  • E. Create a scheduled task that automatically identifies sensitive data based on regular expressions and applies the appropriate tags. This automates the classification process.

Answer: B,C

Explanation:
Defining tag schemas at the account level (Option A) ensures consistency in tag definitions across the entire Snowflake account, including all regions. This is a best practice for managing tags in a multi-region environment. When replicating data between regions (Option C) using database replication or failover groups, the tags are automatically replicated along with the data, assuming the tagging schema is included in the replication configuration. Option B describes a valid approach to tag application automation, but it isn't a core best practice related to multi- region replication and tag management. Option D is incorrect because granting the ACCOUNTADMIN role provides excessive privileges and is not a recommended practice. Option E is incorrect because tag names need only be unique within their schema.


NEW QUESTION # 81
You are using Snowpipe with an external function to transform data as it is loaded into Snowflake. The Snowpipe is configured to load data from AWS SQS and S3. You observe that some messages are not being processed by the external function, and the data is not appearing in the target table. You have verified that the Snowpipe is enabled and the SQS queue is receiving notifications. Analyze the following potential causes and select all that apply:

  • A. The IAM role associated with the Snowflake stage does not have permission to invoke the external function. Verify that the role has the necessary permissions in AWS IAM.
  • B. The Snowpipe configuration is missing a setting that allows the external function to access the data files in S3. Ensure that the storage integration is configured to allow access to the S3 location.
  • C. The data being loaded into Snowflake does not conform to the expected format for the external function. Validate the structure and content of the data before loading it into Snowflake.
  • D. The AWS Lambda function (or other external function) does not have sufficient memory or resources to process the incoming data volume, leading to function invocations being throttled and messages remaining unprocessed.
  • E. The external function is experiencing timeouts or errors, causing it to reject some records. Review the external function logs and increase the timeout settings if necessary.

Answer: A,C,D,E

Explanation:
When using Snowpipe with external functions, several factors can cause messages to be dropped or unprocessed. The most common include external function errors or timeouts (A), permission issues between Snowflake and the external function (B), data format mismatches (C), and the external function lacking resources (E) leading to throttling. Option D is less likely, as the storage integration is primarily for COPY INTO and not direct Lambda function calls, assuming the Lambda function retrieves the data directly from S3 using the event data provided by SQS. The permissions issue B is still relevant as the lambda function will need access to the files in S3.


NEW QUESTION # 82
You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

  • A. Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
  • B. Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
  • C. Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
  • D. Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
  • E. Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.

Answer: A,B

Explanation:
Options C and E are the most appropriate and secure. Option C leverages 'snowflake.snowpark.functions.lit()' to safely incorporate literal values into the SQL query. The 'lit()' function handles the proper escaping, mitigating potential SQL injection risks. The resulting query is then executed with 'session.sql()'. This solution is very useful when using 'SP' and 'UDTF. Option E uses to create a SQL expression object that safely constructs the 'WHERE clause using string formatting, reducing the risk of SQL injection. session.sql()' is then used to execute the constructed query. Options A and B are generally unsafe due to SQL injection vulnerabilities. Option D may not be efficient for larger category lists and is less readable.


NEW QUESTION # 83
......

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