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

1. You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?

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

2. A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A) Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
B) Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.
C) Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
D) Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.
E) Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.

3. You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

A) Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
B) Using followed by is an efficient approach to calculate unique users per page per day.
C) Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
D) Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
E) Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.

4. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
B) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
C) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
D) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
E) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.

5. A data engineer is tasked with creating a Listing to share a large dataset stored in Snowflake. The dataset contains sensitive Personally Identifiable Information (PII) that must be masked for certain consumer roles. The data engineer wants to use Snowflake's dynamic data masking policies within the Listing to achieve this. Which of the following approaches is the MOST secure and maintainable way to implement this requirement, assuming that the consumer roles are pre-defined and known?

A) Create multiple versions of the shared tables, each with different masking applied. The data engineer must manually manage which version each consumer can access.
B) Implement an external function that masks the data based on the consumer's role and share this function in the Listing. Use this external function in a view shared through the listing.
C) Apply dynamic data masking policies directly to the base tables containing the PII and share these tables in the Listing. Policies should use the function to determine when to mask the data.
D) Create a view that applies conditional masking using 'CASE' statements based on the function and share the view in the Listing.

Solutions: