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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q126-Q131):
NEW QUESTION # 126
You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.
Answer: A,B
Explanation:
Options A and E, both provides optimized performance, A pre-computes the aggregated and joins then cluster the data, the use of a materialized view to pre-calculate fulfillment times and grouping by region significantly speeds up queries. Clustering by 'REGION NAME further optimizes queries filtered by region. E, Incremental refreshes are crucial for maintaining data freshness with minimal performance impact. Because incremental refreshes do not support partition. Option B is not performant if we don't do any clustering on the MV. Option C does not support incremental refresh and its not good in this case. Option D partitioning the original table has no impact on MV query performance.
NEW QUESTION # 127
You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?
Answer: B
Explanation:
Option D is the most complete and reliable approach. It first creates a backup table in production using Time Travel before the transformation. Then, clones both the original (pre-transformation) table and the transformed table into the development environment. Finally, it compares these cloned tables to validate the transformation. The use of LAST_QUERY_ID() would not be suitable since the clone is required in the same session, while TIMESTAMP based approach is less reliable due to the lack of synchronisation on when the query was executed.
NEW QUESTION # 128
A provider account is sharing a database named 'SHARED DB' through a share named 'MY SHARE. The consumer account has created a database named 'CONSUMER DB' from the share. The provider account revokes access to a table named 'SALES DATA within 'SHARED DB'. What will happen when a user in the consumer account attempts to query 'CONSUMER DB.SHARED SCHEMA.SALES DATA'?
Answer: C
Explanation:
When access to a shared object is revoked in the provider account, the consumer account loses access to that object. Subsequent queries in the consumer account will fail with an error message indicating insufficient privileges or that the object does not exist. Snowflake does not automatically redirect queries to other shares or provide cached result once access is revoked.
NEW QUESTION # 129
A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?
Answer: D,E
Explanation:
Explanation: Option B identifies a key cause: timezone mismatches between Spark and Snowflake. Ensuring these are aligned is essential. Option D proposes converting 'TIMESTAMP NTZ to strings using 'sfTimestampNTZAsString' is the correct approach to preserve precise values, and setting the Spark session timezone is also critical. Option A is incorrect since there are parameters. Option C involves unnecessary data transformation within Snowflake which is what we are trying to avoid with the connector. Setting 'sfTimestampNTZAsString' to false causes conversion which lead to data issue.
NEW QUESTION # 130
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 # 131
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