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Snowflake DEA-C02 Practice Test - Free Updated Demo (2025)

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q12-Q17):

NEW QUESTION # 12
You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A. Prompt the user for the PostgreSQL username and password each time the script is executed.
B. Hardcode the PostgreSQL username and password directly into the Python script.
C. Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.
D. Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
E. Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.

Answer: C

Explanation:
Option D, using a dedicated secrets management service, provides the most secure and manageable approach. Secrets management services are designed to securely store and manage sensitive information like database credentials. They offer features like encryption, access control, auditing, and versioning, making them the best choice for production environments. Option A is highly insecure. Options B and C are better than A but still less secure than using a secrets management service, as environment variables and configuration files can be accidentally exposed or committed to version control. Option E is impractical and insecure for automated pipelines.

NEW QUESTION # 13
You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)

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

Answer: A,B,D

Explanation:
Options A, B and E are valid ways to selectively load the data. Option A: specifies the full path to the desired file directly in the FROM clause. Option B: uses the 'FILES' parameter to explicitly list the file to be loaded. Option E: uses PATTERN regular expression to filter the files. Option C is incorrect because the 'WHERE clause is invalid in 'COPY' command. Option D is wrong as it's not a correct directory structure, and also invalid as it is trying to specify folders with year, month, day structure.

NEW QUESTION # 14
You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A. Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
B. 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.
C. Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
D. Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
E. Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.

Answer: E

Explanation:
Option C is the most efficient and self-contained approach using native Snowflake features. Embedding the retry logic within the stored procedure called by 'task_ff allows for fine-grained control over the retry process, exception handling, and delay implementation. The retry count limit prevents infinite loops. Option A, while technically feasible, involves querying the TASK HISTORY view, which can be less efficient. Option B requires creating and managing an additional task. Option D introduces external dependencies, making the solution more complex. Option E does not address the retry mechanism.

NEW QUESTION # 15
You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

Answer: E

Explanation:
Option D is the best solution as it effectively uses 'SHA2 for the hashed email for support, 'SUBSTRING(email, POSITION('@' IN email) + 1)' correctly extracts the domain, and LENGTH(phone)-4), RIGHT(phone, 4))' creates the 'XXXXXXXXXX' and then the final four digits of the phone. Other options are not correct because they may use incorrect functions, or because they use outdated syntax (CONCAT instead of 'IF). The correct solution uses the correct functions, SHA2()' for Email Hash for support User, 'SUBSTRING(email, POSITION('@' IN email) + 1)' extract Domain name of the Email for Marketing User, LENGTH(phone)-4), RIGHT(phone, 4))' masking the Phone number by preserving last four digits for Support User.

NEW QUESTION # 16
You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?

A. Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
B. Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
C. Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
D. Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
E. Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.

Answer: B

Explanation:
The ' EXPLAIN' command provides detailed information about the query execution plan. By examining the 'partitionsTotal' and 'partitionsScanned' values, you can directly see how many micro-partitions Snowflake considered vs. how many it actually scanned. A large difference suggests that the clustering is effectively pruning partitions based on the 'EVENT_TIMESTAMP' filter. While 'SYSTEM$CLUSTERING_INFORMATION' provides a general overview of clustering quality, it doesn't tell you how it's performing for a specific query. Looking at query history or checking that the clustering key is defined is useful for verifying basic setup but doesn't directly diagnose the effectiveness for slow queries.

NEW QUESTION # 17
......

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