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NEW QUESTION # 146
You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?
Answer: B
Explanation:
Option E correctly implements exponential backoff with jitter. It calculates the delay using 'base_delay 2 attempt (exponential backoff) and adds a random jitter using 'random.uniform(0, 1)'. It also handles non rate-limiting exceptions by re-raising the exception if it's not caused by rate limiting. Option A would fail to re-raise an error other than RateLimitException. Option B lacks jitter. Option C lacks both jitter and correct exponential backoff calculation. Option D does not use exponential backoff and also lacks retry logic. Therefore, option E is the correct answer.
NEW QUESTION # 147
You're tasked with building an external function in Snowflake that calls an API to enrich customer data with geographical information (latitude and longitude) based on their IP address. The API endpoint requires an API key passed in the headen Your external function definition looks like this: "'sql CREATE OR REPLACE EXTERNAL FUNCTION VARCHAR) RETURNS VARIANT VOLATILE MAX BATCH ROWS = 100 RETURNS NULL ON NULL INPUT API INTEGRATION = AS 'https://api.example.com/geo'; Which of the following steps are essential to ensure the external function correctly passes the API key to the external service, handles rate limiting from the API, and correctly parses the JSON response from the external service (Assume the API returns a JSON object with 'latitude' and 'longitude' fields)?
Answer: A,B,D,E
Explanation:
The correct answers are A, B, C and E. Option A is necessary to establish trust between Snowflake and the external service. Option B ensures the API key is securely passed, rate limiting is handled gracefully, proper exception handling is implemented, and JSON is properly parsed. Option C provides proper privleges. Option E provide proper monitoring. Option D is incorrect because embedding API keys in the URL is a security vulnerability.
NEW QUESTION # 148
You need to implement both a row access policy and a dynamic data masking policy on the 'EMPLOYEE table in Snowflake. The requirements are as follows: 1. Employees should only be able to see their own record in the 'EMPLOYEE table. 2. The 'SALARY' column should be masked for all employees except those with the 'HR ADMIN' role. Unmasked values are required for compliance reasons, they need to be available for 'HR ADMIN' role. Given the following table structure: CREATE TABLE EMPLOYEE ( EMPLOYEE ID INT, EMPLOYEE NAME STRING, SALARY NUMBER, EMAIL STRING ) ; Which of the following sets of steps correctly implement the row access policy and dynamic data masking policy?
Answer: A
Explanation:
Option B implements both policies correctly. The row access policy correctly checks if the 'EMPLOYEE ID matches the 'CURRENT_USER()'. Although the use of is not correct in this situation, it is being used with 'employee_id' so can only see his own record in the 'EMPLOYEE table. The masking policy uses 'CURRENT correctly to check if the role in the session is 'HR_ADMIN'. If it is, the original salary value is returned; otherwise, it masks it to Other masking policy options will return a string representation ("MASKED") or return a hash of the value, which is not a valid 'NUMBER. Option A uses IS ROLE IN SESSION rather than CURRENT_ROLE. 'CURRENT_ROLE only returns the primary role used to initialize the session whereas will return TRUE if the role is the primary role or any of the active secondary roles in the current session.
NEW QUESTION # 149
You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform real-time aggregations, and store the results in a Snowflake table. The data stream is highly variable, with occasional spikes in traffic that overwhelm your current Snowpark setup, leading to significant latency in processing. Which of the following strategies, either individually or in combination, would be MOST effective to handle these traffic spikes and ensure near real-time processing?
Answer: B,D
Explanation:
Options A and D offer the best approach. Implementing a message queue (A) provides a buffer for incoming data during spikes, preventing your Snowpark application from being overwhelmed. Dynamic warehouse scaling (D) allows you to automatically increase the compute resources available to your Snowpark application when needed, ensuring it can handle the increased workload. Auto suspend/resume (B) is good for cost optimization but doesn't address the processing capacity during spikes. Async actions (C) can help, but are not as scalable or resilient as a proper message queue combined with dynamic warehouse scaling. Caching results (E) is irrelevant since the data from Kafka is always changing.
NEW QUESTION # 150
You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?
Answer: A
Explanation:
Configuring ERROR = 'SKIP FILE'' will ensure that Snowpipe skips any file containing errors and continues processing other valid files. Using the 'VALIDATION MODE' metadata column in the external table allows you to identify which files were skipped due to errors. While custom error handlers could be used, using Snowpipe built-in feature with metadata column is more simpler and effective for the task. Validate function needs a job_id and it is not commonly used for external stages. 'ON ERROR = 'ABORT STATEMENT'' will cause pipeline to stop and hence is less preferable.
NEW QUESTION # 151
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