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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You're building a data product on the Snowflake Marketplace that includes a view that aggregates data from a table containing Personally Identifiable Information (PII). You need to ensure that consumers of your data product CANNOT directly access the underlying PII data but can only see the aggregated results from the view. What is the MOST secure and recommended approach to achieve this?

A) Grant the 'SELECT privilege only on the to the share used for the Marketplace listing. Do not grant any privileges on the underlying PII table.
B) Create a stored procedure that returns the aggregated data, and grant EXECUTE privilege on the stored procedure to the share. The stored procedure SELECTs from the PII table.
C) Grant USAGE privilege on the database containing the PII table and to the share.
D) Grant the 'SELECT privilege directly on the underlying PII table to the share used for the Marketplace listing, along with the 'SELECT privilege on 'sensitive data view'.
E) Grant 'READ privilege on the internal stage containing the data files backing the PII table.

2. You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A) Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
B) Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.
C) Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
D) Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
E) Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.

3. A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

A) Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
B) Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
C) Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.
D) Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
E) Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.

4. You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

A) Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
B) Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
C) Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
D) Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
E) Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE

5. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Create a materialized view on top of the external table to pre-aggregate the data.
B) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
C) Increase the size of the Snowflake virtual warehouse to provide more processing power.
D) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
E) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.

Solutions: