Snowflake DEA-C02 valid study dumps : SnowPro Advanced: Data Engineer (DEA-C02)

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
B) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
C) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
D) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
E) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.

2. 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) Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
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) 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.
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.

3. 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) 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.
B) 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.
C) Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.
D) Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.
E) Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.

4. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
B) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
C) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
D) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
E) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.

5. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

Solutions: