Snowflake SnowPro Advanced DEA-C02

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 questions d'examen:

1. You are designing a data sharing solution for a multi-tenant application where each tenant's data must be isolated. You have a 'sales' table with a 'tenant_id' column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

A) Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.
B) Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.
C) Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.
D) Implement a user-defined function (UDF) that checks the current user's tenant ID and returns a boolean value indicating whether the row should be visible. Use this UDF in a WHERE clause in every query.
E) Implement a row access policy on the 'sales' table that filters data based on the 'tenant_id' column and the current role or user context.

2. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
B) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
C) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
D) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
E) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.

3. A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?

A) Implement a stored procedure that leverages external functions to call a Python script hosted on AWS Lambda, which uses a machine learning model to identify PII and apply Snowflake tags.
B) Use Snowflake's Event Tables in conjunction with a stream and task. Configure the stream to capture DDL changes, and the task to evaluate new columns and apply the tag based on the column metadata using regular expressions.
C) Create a Snowflake Task that runs daily, querying the INFORMATION SCHEMCOLUMNS view, identifying potential PII columns based on regular expressions, and then executing ALTER TABLE ... ALTER COLUMN ... SET TAG commands.
D) Manually tag each column containing PII using the Snowflake web UI or the 'ALTER TABLE ... ALTER COLUMN ... SET TAG' command. Train data stewards to identify and tag new columns.
E) Implement a custom application using the Snowflake JDBC driver to periodically scan table schemas, detect PII columns, and apply tags using dynamic SQL.

4. You are tasked with creating a development environment from a production database named 'PROD DB'. This database contains sensitive data, and you need to mask the data in the development environment. You decide to use cloning and a transformation function during the cloning process. What is the MOST efficient approach to clone 'PROD DB' into a development database 'DEV DB' and mask sensitive data in the process?

A) Create a clone of 'PROD named 'DEV DB'. Define masking policies on the columns in 'PROD DB' before cloning. These policies will be automatically applied to the cloned tables in "DEV_DB' ensuring all data is masked during query time in the DEV environment.
B) Create a clone of 'PROD named 'DEV DB'. Create stored procedures on 'DEV DB' which apply masking at the query level. Cloning databases does not preserve masking policies from the Source database
C) Clone 'PROD to ' DEV DB'. Export the data from 'DEV DB', transform it using a scripting language (e.g., Python), and then load the transformed data back into replacing the original data.
D) Create a clone of 'PROD named 'DEV DB'. Create a warehouse for running masking policies. Then apply masking policies to the tables in 'DEV DB' Cloning masks the underlying data directly.
E) Create a clone of 'PROD named 'DEV DB', then create views on 'DEV DB' using masking policies. Cloning the Views from 'PROD will automatically copy the masking policies.

5. You are tasked with calculating the daily moving average of sales for each product category in your Snowflake data warehouse using Snowpark Python. You need to handle cases where there might be missing sales data for certain days. You have the following sales data available in a table named with columns: (DATE), (VARCHAR), and 'sales_amount' (NUMBER). Which of the following Snowpark Python code snippets correctly calculates the 7-day moving average of sales amount per product category, filling in missing sales days with 0, and handles potential division by zero?

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

Questions et réponses: