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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. 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'?

A) The query will execute successfully, but the user will receive an empty result set.
B) The query will execute successfully, but only return data that existed before the access was revoked.
C) The query will be automatically re-routed to another available share containing 'SALES DATA'.
D) The query will be cached based on the initial access, so users can continue query previous result based on same SQL
E) The query will fail with an error message indicating that the table does not exist or the user does not have privileges.

2. You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A) Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
B) Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
C) Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
D) Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
E) Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.

3. You are designing a data protection strategy for a Snowflake environment that processes sensitive payment card industry (PCI) data'. You decide to use a combination of column-level security and external tokenization. Which of the following statements are TRUE regarding the advantages of using both techniques together? (Select TWO)

A) Masking policies and external tokenization provide independent layers of security. If one is compromised, the other still provides protection.
B) The use of both techniques increases query performance drastically.
C) Combining masking policies and external tokenization allows for complete elimination of PCl data from the Snowflake environment, even during processing.
D) Column-level security can be used to restrict access to the tokenization UDF itself, ensuring that only authorized users can perform tokenization or detokenization operations.
E) Tokenization ensures compliance with PCl DSS standards, while masking policies are primarily useful for internal access control and obfuscation for development environments. Using both doesn't increase security.

4. A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

A) Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.
B) Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
C) Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
D) Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
E) Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.

5. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

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

Solutions: