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

1. You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

2. You have a large dataset stored in AWS S3 in Parquet format. The data is constantly updated by an external process, but you need to run read-only analytical queries against the most current data in Snowflake without ingesting it. Which approach is the MOST efficient and cost-effective way to achieve this, considering minimal latency for query results?

A) Create a Snowflake external table pointing to the S3 location. Manually refresh the external table metadata periodically using the 'ALTER EXTERNAL TABLE REFRESH' command.
B) Use Snowpipe to continuously ingest the Parquet files from S3 into a Snowflake table.
C) Create a view on top of the S3 files using a custom Java UDF to read Parquet data directly. Then, query the view.
D) Create an external table in Snowflake pointing to the S3 location. Configure automatic data refresh using the 'AUTO REFRESH' parameter and a notification integration.
E) Create a Snowflake internal stage, load the Parquet files into the stage periodically, and create a table from the staged files.

3. You are using Snowflake Iceberg tables to manage a large dataset stored in AWS S3. Your team needs to perform several operations on this data, including updating existing records, deleting records, and performing time travel queries to analyze data at different points in time. Which of the following statements regarding the capabilities and limitations of Snowflake Iceberg tables are TRUE? (Select all that apply)

A) Snowflake automatically manages the Iceberg metadata, including snapshots and manifests, eliminating the need for manual metadata management tasks.
B) Snowflake Iceberg tables support time travel queries using the 'AT(timestamp => ...y syntax, allowing you to query the state of the data at a specific point in time.
C) Snowflake Iceberg tables support 'UPDATE, ' DELETE, and 'MERGE operations, allowing you to modify existing data directly in the data lake.
D) Snowflake Iceberg tables support both row-level and column-level security policies, allowing you to control access to sensitive data at a granular level.
E) Snowflake Iceberg tables do not support transaction isolation levels, so concurrent write operations may lead to data inconsistencies.

4. You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A) Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
B) Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
C) Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
D) Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
E) Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.

5. A data engineering team is implementing a change data capture (CDC) process using Snowflake Streams on a table 'CUSTOMER DATA'. After several days, they observe that some records are missing from the target table after the stream is consumed. The stream 'CUSTOMER DATA STREAM' is defined as follows: 'CREATE STREAM CUSTOMER DATA STREAM ON TABLE CUSTOMER DATA;' and the transformation code to process the data is shown below. What could be the possible reasons for the missing records, considering the interaction between Time Travel and Streams? Assume all table sizes are significantly larger than micro-partitions, making full table scans inefficient.

A) The parameter for the database containing 'CUSTOMER_DATR is set to a value lower than the stream's offset persistence, causing some changes to be purged before the stream could consume them.
B) The stream's 'AT' or 'BEFORE clause in the consumer query is incorrectly configured, causing it to skip some historical changes.
C) DML operations (e.g., DELETE, UPDATE) performed directly against the target table are interfering with the stream's ability to track changes consistently.
D) The underlying table 'CUSTOMER DATA' was dropped and recreated with the same name, invalidating the stream's tracking capabilities.
E) The stream's offset persistence is reliant on Time Travel, If the data being ingested is older than the set Time Travel duration, the change may not be seen by the stream.

Solutions: