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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are planning to monetize a dataset on the Snowflake Marketplace. You want to provide potential customers with sample data to evaluate before they purchase a full subscription. Which of the following strategies are valid and recommended for offering a free sample of your data within the Snowflake Marketplace? (Select all that apply)

A) Provide the consumer with the script to create a database link to your data, allowing them read-only access to a pre-defined sample table, and then revoke the access after a set period.
B) Upload a sample CSV file to a publicly accessible S3 bucket and provide the link in the Marketplace listing description. Consumers can download and load this data into their own Snowflake account for evaluation.
C) Create a separate share containing a subset (e.g., a smaller number of rows or columns) of the full dataset and offer this share as a free trial listing on the Marketplace.
D) Offer a 'free trial' subscription on the primary listing that automatically expires after a set period (e.g., 7 days), allowing customers to access the full dataset during the trial period. You will need to write custom code to manage trial expiration and data access restrictions based on the trial status.
E) Create a view that filters the dataset based on a sampling algorithm (e.g., 'SAMPLE ROW' clause) and share the view through the Marketplace.

2. You need to implement a data masking policy on the 'EMAIL' column of the 'EMPLOYEES' table. The requirement is to redact the entire email address with 'XXXXX' if the user's role is 'PUBLIC'. If the user's role is 'ANALYST', the domain part of the email should be visible, but the username should be redacted. For all other roles, the full email should be visible. Which of the following SQL statements CORRECTLY implements this masking policy?

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

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

4. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
B) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
C) Set 'snowflake.ingest.file.name' to an existing file in a stage.
D) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
E) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.

5. You are troubleshooting a slow-running query that joins a large fact table 'SALES DATA' (100 billion rows) with a smaller dimension table 'CUSTOMER DIM' (1 million rows) on 'CUSTOMER ID. Initial analysis shows that the query is spending significant time in the join operation. You suspect the issue lies with the join strategy being used by Snowflake. Which of the following actions are MOST likely to improve query performance and optimize the join?

A) Ensure both 'SALES DATA' and 'CUSTOMER DIM' are clustered on 'CUSTOMER ID.
B) Increase the virtual warehouse size and monitor for spillover to local disk. If spilling occurs, further increase the warehouse size.
C) Analyze the query profile in Snowflake's web UI and identify if a broadcast join is occurring. If so, consider increasing session parameter (within limits) or re-designing the query to avoid the broadcast join.
D) Convert the query to use a LATERAL FLATTEN function to pre-process the 'CUSTOMER_DIW table before the join.
E) Ensure that the 'CUSTOMER_ID column in both tables has compatible datatypes and that no implicit type conversions are happening during the join. Also check cardinality of 'CUSTOMER_ID in the SALES DATA table.

Solutions: