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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?

A) Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.
B) Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
C) Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
D) Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.
E) Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.

2. You have a Snowflake table called 'ORDERS' with a column named 'ORDER DETAILS that contains JSON data'. You need to create a UDF to extract a specific value from the JSON and return it. The JSON structure is consistent: '{'customer': {'name': 'John Doe', 'email': '[email protected]'}, 'items': [{'item id': 1, 'quantity': 2}, {'item id': 2, 'quantity': You want to extract the customer's email address. Which of the following UDF definitions is the most efficient and correct?

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

3. You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

A)

B)

C)

D)

E)

4. You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

A) Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.
B) Add a search optimization strategy to the table on the 'event_time' column.
C) Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
D) The table is already partitioned by 'event_time' , so there is no need for further optimization.
E) Increase the warehouse size to improve query performance.

5. Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A) Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
B) Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.
C) Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
D) Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
E) Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.

Solutions: