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

1. A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested daily into Snowflake. The ingestion process currently uses a single 'X-Large' virtual warehouse. During peak hours, the data loading latency increases significantly, impacting downstream reporting. The team is considering either scaling up to a '3X-Large' warehouse or scaling out by creating a multi- cluster warehouse with a minimum of 2 and a maximum of 4 'X-Large' clusters. Which of the following factors should be prioritized when making this decision to optimize performance, considering cost and concurrency requirements?

A) The impact on other workloads sharing the same virtual warehouse, favoring scaling up to isolate the data loading process from other query activities.
B) The complexity of the JSON data and the potential for improved parsing performance by a larger, single warehouse due to increased memory.
C) The type of JSON processing used. If using SQL functions like PARSE JSON, scaling out is more beneficial than scaling up.
D) The budget constraints and the higher per-second cost of a larger warehouse compared to the potential cost savings from reduced overall execution time.
E) The anticipated number of concurrent data loading jobs and the ability of Snowflake to automatically distribute these jobs across multiple clusters.

2. A data pipeline ingests clickstream data from various sources into a raw Snowflake table CRAW CLICKS). A transformation job then processes this data and loads it into a more structured 'CLICK EVENTS table, performing filtering, cleaning, and data enrichment. The data engineering team notices significant performance bottlenecks during this transformation process, leading to data freshness issues.
The team wants to optimize this process, considering the following:

A) Use a larger virtual warehouse for the transformation job and partition the 'RAW CLICKS table on the ingestion timestamp to improve data pruning and reduce the amount of data processed during the transformation.
B) Create a materialized view on top of 'RAW CLICKS' that pre-computes the necessary transformations and aggregations, allowing the 'CLICK EVENTS' table to be populated directly from the materialized view.
C) Replace the transformation job with a series of smaller, more specialized jobs, each running on a separate virtual warehouse optimized for the specific task, and orchestrate these jobs using a data pipeline tool.
D) Optimize the transformation queries by identifying and rewriting inefficient SQL patterns, ensuring appropriate use of joins, filtering conditions, and data type conversions.
E) Implement a change data capture (CDC) mechanism on the source systems to only ingest changed data into 'RAW CLICKS, reducing the overall data volume and the amount of data processed by the transformation job.

3. 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 E
B) Option B
C) Option C
D) Option A
E) Option D

4. Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

A) Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
B) Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.
C) Dynamic Data Masking policies can reference external tables directly without requiring special grants.
D) Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
E) Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.

5. You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A) Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
B) Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
C) Executing 'df.collect()' to load the entire table into the client's memory before filtering.
D) Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.
E) Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE

Solutions: