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

1. You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A) The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.
B) The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.
C) The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
D) There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.
E) The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.

2. You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

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

3. You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

A)

B)

C)

D)

E)

4. You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

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

5. You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

A) Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.
B) Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.
C) Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.
D) Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
E) Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.

Solutions: