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NEW QUESTION # 131
You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?
Answer: B
Explanation:
Option B is the most comprehensive solution for secure data loading and PII protection. 'TRANSFORM' and JavaScript UDF masking during load prevent PII from being stored unmasked, enhancing security. Implementing masking policies provides granular control over access to the sensitive data post-load. It's more secure to avoid storing sensitive information even temporarily. Option A encrypts the data in transit but doesn't address masking. Option C requires dynamic data masking, but masking during copy is optimal. The correct way to copy and mask PII data is using a JavaScript UDF to mask the PII data during the load process, and then implement Masking Policies on the table.
NEW QUESTION # 132
Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?
Answer: B,E
Explanation:
Options A and C are the most effective strategies. Incremental data loading (Option A) focuses on processing only the changed data, significantly reducing the processing time and resources used. Cloning and swapping (Option C) can provide a faster refresh while maintaining data availability (with a brief interruption during the swap). Option B, while faster than 'CREATE OR REPLACE TABLE, is still a full refresh and inefficient. Option D only mitigates the impact, not the underlying inefficiency. Option E will help improve performance but can be costly, should only be implemented for specific columns/tables and does not reduce the need for optimizing the data pipeline's refresh strategy directly.
NEW QUESTION # 133
You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)
Answer: A,B,D,E
Explanation:
Options A, B, C and E are all helpful for monitoring stream lag. 'SYSTEM$STREAM HAS DATA' confirms the presence of changes. 'CURRENT_TIMESTAMP' vs. directly measures latency. Analyzing query history identifies blocking consumers. Monitoring warehouse resources can reveal bottlenecks in processing stream data. Increasing 'DATA RETENTION_TIME IN_DAYS (D) for the base tables is irrelevant to stream lag and affects table history, not stream processing performance. It does not address the issue of why the stream is lagging.
NEW QUESTION # 134
You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:
Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?
Answer: A
Explanation:
Using 'functools.lru_cache' (Option A) is the most efficient and straightforward solution. It provides a built-in caching mechanism within the Python UDF's scope without requiring external dependencies or complex manual caching logic. Option B is not the best, as it will cause issues in multithreaded environment where this is not thread safe and could cause data inconsistency. Option C is related to Snowflake result cache which is independent of UDF cache needs and concerns. The temp table (option D) adds overhead by querying external tables within the UDF, making API execution slower rather than faster. And Option E needs external connections which increase infrastructure complexity.
NEW QUESTION # 135
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:
Answer: B,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 # 136
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