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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q310-Q315):

NEW QUESTION # 310
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: B

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 Other options are not correct because they may use incorrect functions, or because they use outdated syntax (CONCAT instead of 'IF).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 # 311
A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A. Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.
B. Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
C. Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
D. Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.
E. Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.

Answer: D

Explanation:
Clustering by 'LOCATION' and 'TIMESTAMP' will group related data together, allowing Snowflake to quickly identify the relevant data for spatial queries. Enabling search optimization on the 'LOCATION' column allows queries filtering by geographic area to be accelerated. This combination provides the best performance because it addresses both the time-based and spatial aspects of the queries. Partitioning isn't directly supported by Snowflake but Clustering plays the equivalent role, and search optimization on Geography objects is critical. Materialized views can help, but might not be flexible enough for ad-hoc analysis. Automatic Clustering on 'DEVICE_ID won't help with spatial or time-based filtering. Search optimization on temperature and pressure will not help in spatial search.

NEW QUESTION # 312
You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A. Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
B. Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
C. Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
D. Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
E. Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.

Answer: A,C,D,E

Explanation:
Options A, B, C and E are all helpful for monitoring stream lag. 'SYSTEM$STREAM HAS DATA' confirms the presence of changes. 'CURRENT_TIMESTAMP' vs. directly measures latency. Analyzing query history identifies blocking consumers. Monitoring warehouse resources can reveal bottlenecks in processing stream data. Increasing 'DATA RETENTION_TIME IN_DAYS (D) for the base tables is irrelevant to stream lag and affects table history, not stream processing performance. It does not address the issue of why the stream is lagging.

NEW QUESTION # 313
You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

A. Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.
B. Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.
C. Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
D. Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.
E. Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.

Answer: A,C,E

Explanation:
Options B, C, and D are correct. Logging intermediate results allows you to track the flow of data and identify the point of failure. Modularizing the code and testing individual functions makes debugging easier. Passing the 'snowflake' binding allows each module to log and execute SQL. Option A is insufficient, complex error requires more granular logging. Option E is dangerous and should be avoided as metadata changes should ideally be performed outside of transformation stored procedure and should be managed by orchestration tools.

NEW QUESTION # 314
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. 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.
B. Grant 'READ privilege on the internal stage containing the data files backing the PII table.
C. 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'.
D. Grant USAGE privilege on the database containing the PII table and to the share.
E. 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.

Answer: E

Explanation:
Granting only 'SELECT privilege on the (option B) ensures that consumers can only access the view and not the underlying PII data. Granting 'SELECT on the underlying table (option A) defeats the purpose of the view. Using a stored procedure (option C), while potentially masking the data access, is less performant and can still expose data if not carefully implemented. 'USAGE privilege (option D) only allows access to the database, not the data itself. 'READ' on the stage (option E) allows direct access to the raw data, which exposes the PII.

NEW QUESTION # 315
......

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2025 RELIABLE LATEST STUDY DEA-C02 QUESTIONS | DEA-C02 100% FREE LATEST EXAM PRICE

2025 Reliable Latest Study DEA-C02 Questions | DEA-C02 100% Free Latest Exam Price