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NEW QUESTION # 87
A Snowflake table 'ORDERS' contains billions of records and is frequently queried for reporting purposes. The reporting queries often filter on 'ORDER DATE and 'CUSTOMER ID'. The data engineering team is considering creating a clustering key to improve query performance. They are evaluating two options: (1) clustering on 'ORDER DATE' alone and (2) clustering on '(ORDER DATE, CUSTOMER ID)'. Which of the following statements best describes the trade-offs between these two options in the context of query performance and data maintenance?
Answer: D
Explanation:
Clustering on multiple columns (in this case, 'ORDER_DATE' and 'CUSTOMER_ID') improves performance for queries that filter on both columns. However, if the distribution of is uneven within the 'ORDER DATE partitions (skew), it can lead to some micro-partitions still containing a large range of 'CUSTOMER_ID' values, requiring Snowflake to scan more data. This can also increase reclustering costs as Snowflake tries to optimize the clustering. Clustering on only may be beneficial if most queries primarily filter on date. Option E is incorrect because clustering is a Snowflake feature that improves performance.
NEW QUESTION # 88
A Snowflake Data Engineer is tasked with identifying all downstream dependencies of a view named 'CUSTOMER SUMMARY. This view is used by multiple dashboards and reports. They want to use SQL to efficiently find all tables and views that directly depend on 'CUSTOMER SUMMARY. Which of the following SQL queries against the ACCOUNT USAGE schema is the MOST efficient and accurate way to achieve this?
Answer: C
Explanation:
The view in the ACCOUNT_USAGE schema is specifically designed to track object dependencies. option B directly queries this view using the and to find objects that depend on the 'CUSTOMER_SUMMARY view. Options A and C rely on parsing 'QUERY_TEXT , which is less reliable and can lead to false positives or misses. Option D looks for base object which has the opposite meaning of finding dependancies on a target view. Option E finds the OBJECT_ID for the view and is unnecessary and introduces an extra step.
NEW QUESTION # 89
You are designing a complex data pipeline in Snowflake that involves multiple interdependent Tasks. Several of these Tasks need to access sensitive customer data, and you want to ensure that the least privilege principle is followed. How should you configure the Tasks and their associated roles to minimize the risk of unauthorized data access while maintaining the functionality of the pipeline? (Select TWO)
Answer: B,C
Explanation:
Following the least privilege principle is crucial for security. Creating specific roles for each Task (B) and granting only the necessary privileges minimizes the risk of unauthorized access. Using stored procedures with 'EXECUTE AS CALLER (C) allows you to control data access through a separate, secured object, further limiting the Task's direct access to sensitive data. Option A grants excessive privileges, violating least privilege. Option D simplifies management but compromises security. Option E makes sensitive data broadly accessible, which is highly insecure.
NEW QUESTION # 90
You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?
Answer: B
Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.
NEW QUESTION # 91
A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.
Answer: B
Explanation:
Snowflake streams can be configured to hide delete operations using the parameten Setting 'HIDE_DELETES = TRUE' will prevent delete operations from being exposed through the stream. Option A is incorrect as streams can be configured. Option B, while functional, adds an extra layer of complexity. Option D doesn't exist as a valid parameter for streams. Option E is a highly unconventional and unsupported approach.
NEW QUESTION # 92
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