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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 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. Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
C. 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.
D. Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
E. 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.

Question 2

A Snowflake table 'ORDERS' is clustered on the 'ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of 'ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

A. Drop and recreate the table with a different clustering key.
B. Change the clustering key to a composite key including ORDER DATE and another relevant column.
C. Change the ORDER DATE column to VARCHAR and then cluster.
D. Increase the virtual warehouse size used for loading data into the "ORDERS' table.
E. Run 'OPTIMIZE TABLE ORDERS' to recluster the table.

Question 3

You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

A. Option E
B. Option C
C. Option B
D. Option A
E. Option D

Question 4

You are tasked with loading a large CSV file (1 T B) into Snowflake. The file contains data for the past 5 years, partitioned by year in the filename (e.g., 'data 2019.csv', 'data 2020.csv', etc.). You need to minimize data loading time and ensure data quality. You have a Snowflake virtual warehouse 'XSMALL' and a stage 'my_stage'. Which of the following strategies would be MOST effective?

A. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = CONTINUE option. Implement data quality checks post-load using SQL queries.
B. Increase the virtual warehouse size to 'LARGE, use a single 'COPY command to load all files with the ERROR = ABORT STATEMENT option. Create a file format with "SKIP HEADER = 1' and 'TRIM SPACE = TRUE.
C. Use Snowpipe with auto-ingest enabled. Ensure your cloud storage event notifications are properly configured. Create a file format with 'SKIP HEADER = 1' and 'TRIM SPACE = TRUE Leave the warehouse as 'XSMALL' to control costs.
D. Load each file individually using a separate 'COPY' command with 'VALIDATION MODE = RETURN ERRORS to check for data quality issues before loading the next file. Use the 'XSMALL' warehouse for all loads.
E. Create multiple named file formats each with a unique 'SKIP HEADER value matching the number of header rows in each file. Load using a single 'COPY' command referencing each file format specifically.

Question 5

You have a Snowflake Stream named 'PRODUCT CHANGES' created on a table 'PRODUCTS'. A downstream task attempts to consume records from the stream, but occasionally fails with a 'Table PRODUCTS has been altered' error. The 'PRODUCTS' table undergoes DDL changes (e.g., adding/dropping columns) infrequently, but these changes are necessary for evolving business requirements. How can you design a more resilient data pipeline that minimizes disruptions caused by DDL changes to the 'PRODUCTS' table while still leveraging the 'PRODUCT CHANGES' stream?

A. Implement error handling in the downstream task to automatically retry consuming records from the 'PRODUCT CHANGES' stream after a delay, assuming the DDL changes will be completed quickly.
B. Before executing any DDL changes on the 'PRODUCTS' table, drop and recreate the 'PRODUCT CHANGES' stream. This ensures the stream definition is always in sync with the table structure.
C. Create a task that monitors the 'PRODUCTS' table for DDL changes using 'INFORMATION SCHEMA. TABLES'. When a change is detected, pause the downstream task, execute the DDL change, and then resume the downstream task after a short delay.
D. Create a new Stream on the 'PRODUCTS' table after each DDL change. The downstream task should dynamically switch to consuming from the new stream when the old stream encounters an error.
E. Use a materialized view instead of a standard view as the source for the stream. Materialized views are less susceptible to issues when the underlying base table changes

Solutions: