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

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

1. A Snowflake data warehouse contains a table 'WEB EVENTS' with columns like 'EVENT ID', 'EVENT TIMESTAMP, 'USER , 'PAGE URL', and 'SESSION ID'. The data engineering team has enabled search optimization on 'PAGE URL' because analysts frequently filter on specific URLs. However, they notice that queries filtering on multiple 'PAGE URL' values (e.g., using 'WHERE PAGE URL IN ('urll', 'ur12', are not performing as well as expected. What are the potential reasons for this behavior, and what strategies can be used to improve performance in this scenario? Select all that apply:

A) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
B) The number of distinct values in the 'PAGE URL' column is very high, leading to a large search access path, making IN list lookups inefficient. Consider clustering by PAGE_URL
C) Search optimization is not designed to efficiently handle IN list lookups with a large number of values. Consider using a temporary table or common table expression (CTE) to pre-filter the data.
D) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.
E) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.

2. You are tasked with creating an external function in Snowflake that calls a REST API. The API requires a bearer token for authentication, and the function needs to handle potential network errors and API rate limiting. Which of the following code snippets demonstrates the BEST practices for defining and securing this external function, including error handling?

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

3. A data engineering team is managing a Snowflake warehouse that supports a high volume of ad-hoc queries from data analysts exploring a large, semi-structured JSON dataset containing website clickstream data'. The query performance is frequently slow, and analysts are complaining about long wait times. The warehouse is already sized appropriately. You have identified that many of the queries filter on nested JSON attributes that are not explicitly indexed. Considering only query acceleration service features, what is the MOST effective approach to improve query performance for these ad-hoc queries without modifying the queries themselves or significantly increasing storage costs?

A) Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.
B) Use a combination of materialized views and query acceleration for best performance.
C) Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.
D) Enable Automatic Clustering on the table to improve data organization based on query patterns.
E) Enable Search Optimization Service for the table containing the JSON data. Selectively enable search optimization on the specific columns and nested paths that are frequently used in WHERE clause predicates.

4. A data engineering team is implementing column-level security on a Snowflake table named 'CUSTOMER DATA containing sensitive PII. They want to mask the 'EMAIL' column for users in the 'ANALYST role but allow users in the 'DATA SCIENTIST role to view the unmasked email addresses. The 'ANALYST role already has SELECT privileges on the table. Which of the following steps are necessary to achieve this using a masking policy?

A) Create a dedicated view on 'CUSTOMER DATA' for analysts with the 'EMAIL' column masked using a CASE statement within the view's SELECT statement. Grant SELECT privilege to the ANALYST role on the view only.
B) Create a masking policy that uses the IS_ROLE_IN_SESSION('ANALYST') function to return a masked value if the analyst role is active in current session and the original value otherwise.
C) Create a masking policy that uses the CURRENT ROLE() function to return a masked value if the current role is 'ANALYST and the original value otherwise.
D) Create a masking policy with a CASE statement that checks the CURRENT ROLE() function to see if it's 'ANALYST'. If true, mask the email; otherwise, return the original email.
E) Create a masking policy that uses the CURRENT_USER() function to check if the current user belongs to the 'ANALYST' role.

5. A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A) Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
B) Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.
C) Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.
D) Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.
E) Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.

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