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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)

A) Data masking policies are supported on external tables.
B) Once a masking policy is applied to a column, the original data is permanently altered.
C) The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.
D) Different masking policies cannot be applied to different columns within the same table.
E) Data masking policies can be applied to both tables and views.

2. You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

A) Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
B) Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
C) Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
D) Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
E) Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.

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

4. You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

A)

B)

C)

D)

E)

5. A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A) Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.
B) Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
C) Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
D) Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
E) Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.

Solutions: