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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. 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) 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.
B) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
C) 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.
D) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
E) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.

2. Consider the following scenario: You are managing a Snowflake environment where users are running various queries with varying resource demands. You observe frequent warehouse resizing operations, leading to performance fluctuations and increased costs. Which of the following strategies, when implemented together, would BEST stabilize warehouse performance and minimize unnecessary resizing?

A) Monitor query history using Snowflake's web interface and identify query patterns that consistently require larger resources; recommend users refactor those queries. Set the warehouse auto-suspend to a very short duration (e.g., 1 minute) to ensure resources are released quickly when idle.
B) Disable auto-suspend for the warehouse to prevent it from shutting down and causing performance delays. Force users to manually resize the warehouse as needed using SALTER WAREHOUSE commands.
C) Implement Resource Monitors to limit the daily credit consumption of the warehouse. Increase the warehouse size to accommodate all possible query demands and set the auto-suspend to a longer duration (e.g., 60 minutes).
D) Enable Query Acceleration Service (QAS) for the warehouse. Set the warehouse size to Medium, regardless of the actual workload demands, and rely solely on QAS to handle performance variations.
E) Implement Query Tagging to categorize queries based on resource consumption. Analyze resource utilization patterns for different query categories. Adjust warehouse size and multi-cluster configuration based on these patterns, ensuring that a reasonable number of concurrent queries for each workload type is met.

3. You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

A) Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.
B) Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
C) Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
D) Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
E) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.

4. You need to implement a data masking solution in Snowflake for a table 'CUSTOMER DATA' containing PII. The requirement is to mask the email address based on the user's role: if the user is in 'ANALYST ROLE , the email address should be partially masked (e.g., 'a @example.com'), otherwise, it should be fully masked (e.g., @ .com'). Which of the following masking policy definitions and subsequent actions will correctly implement this?

A) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, fully mask using 'REGEXP REPLACE. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
B) Create a masking policy 'email_mask' using 'REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not 'ANALYST_ROLE' , otherwise use 'LEFT and ' REGEXP_REPLACE to mask only part of the username. Apply this policy to the 'EMAIL ' column of 'CUSTOMER DATA'.
C) Create two separate masking policies, one for 'ANALYST_ROLE' and one for all other roles. Apply both policies to the 'EMAIL' column of 'CUSTOMER DATA'. Grant the 'APPLY MASKING POLICY privilege on the 'CUSTOMER DATA' table to the 'ANALYST_ROLE.
D) Create a masking policy 'email_mask' using a 'CASE' statement that checks 'CURRENT_ROLE()'. If the role is 'ANALYST_ROLE, partially mask using 'LEFT and 'REGEXP REPLACE; otherwise, return original value. Apply this policy to the 'EMAIL' column of 'CUSTOMER DATA'.
E) Create a masking policy 'email_mask' that always fully masks the email address. Grant the 'UNMASK' privilege on the 'EMAIL' column to the 'ANALYST ROLE

5. A healthcare provider stores patient data in Snowflake, including 'PATIENT ID', 'NAME, 'MEDICAL HISTORY , and 'INSURANCE ID. They need to comply with HIPAA regulations. As a data engineer, you need to ensure that PHI (Protected Health Information) is masked appropriately based on user roles. Which of the following steps are NECESSARY to achieve this using Snowflake's data masking features and RBAC? (Select all that apply)

A) Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
B) Apply the created masking policies to the corresponding columns in the patient data tables, ensuring that the masking policies are designed to reveal only the necessary information based on the user's role (e.g., doctors see full medical history, nurses see limited medical history, admins see de-identified data).
C) Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.
D) Grant the 'OWNERSHIP privilege on the 'PATIENT table to the 'ACCOUNTADMIN' role, ensuring complete control and management of the data by the administrator.
E) Create custom roles representing different user groups within the organization (e.g., 'DOCTOR, 'NURSE, 'ADMIN') and grant them the necessary privileges to access the data, including 'SELECT on the tables and views containing patient data.

Solutions: