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

1. 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 clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
B) Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
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) 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.
E) 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.

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

3. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
B) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
C) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
D) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
E) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.

4. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

5. You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

A) Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
B) Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
C) Create a secure view that joins the tables and share only the secure view using a data share.
D) Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
E) Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.

Solutions: