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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Transformation and Processing	- Streams and Tasks for ELT pipelines - Handling semi-structured data (JSON, Avro, Parquet) - SQL-based transformations in Snowflake
Topic 2: Performance and Optimization	- Clustering and partition strategies - Query optimization techniques - Warehouse sizing and scaling
Topic 3: Security and Data Governance	- Role-based access control (RBAC) - Secure data sharing - Data masking and encryption
Topic 4: Data Engineering Fundamentals	- Data pipelines concepts and patterns - Snowflake architecture for data engineering
Topic 5: Data Ingestion and Integration	- Snowpipe usage and automation - Batch and streaming ingestion approaches - Staging data and loading mechanisms

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

Question #1
You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

A. Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.
B. Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
C. Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.
D. Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.
E. Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.

Question #2
A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A. Restore the entire Snowflake account to a previous point in time before the table was truncated.
B. Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
C. Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.
D. Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
E. Contact Snowflake support and request them to restore the table from a system-level backup.

Question #3
You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

A. Option C
B. Option D
C. Option A
D. Option E
E. Option B

Question #4
You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

A. Option C
B. Option D
C. Option A
D. Option E
E. Option B

Question #5
You are developing a JavaScript stored procedure in Snowflake using Snowpark to transform data'. The procedure needs to efficiently calculate the sum of 'amount' for each 'customer_id' in a large table named 'orders'. You want to avoid transferring large amounts of data to the client and utilize Snowpark's pushdown capabilities. Which of the following JavaScript code snippets is the MOST efficient and correct way to achieve this? Assume 'snowflake' binding is available.

A.

B.

C.

D.

E.