Snowflake SnowPro Advanced DEA-C02

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Snowflake DEA-C02 Prüfungsthemen:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Prüfungsfragen mit Lösungen

1. You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

A)

B)

C)

D)

E)

2. You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

A) Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
B) Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
C) A and B
D) Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.
E) Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.

3. You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

A)

B)

C)

D) Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.
E)

4. You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

A) Option B
B) Option C
C) Option D
D) Option E
E) Option A

5. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Increase the size of the Snowflake warehouse used for the Snowpark session.
B) Disable auto-tuning for the warehouse to ensure consistent performance
C) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
E) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.

Fragen und Antworten: