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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q256-Q261):

NEW QUESTION # 256
You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

Answer: A

Explanation:
Option D is the most efficient because it filters access based on roles in the RAP without applying expensive UDFs within the policy itself. This minimizes the performance impact of the RAP. The view 'analyst_view' then applies the masking/hashing for analysts. Options A and B apply the UDFs within the RAP, which will significantly degrade performance. The 'MASK EMAIL(email_address) IS NOT NULL' conditions are also incorrect as they are not validating the email. Option C doesn't implement the required masking/hashing for analysts at all, and also is not as effecient. Option E allows both roles to see all data which does not meet requirement 1.

NEW QUESTION # 257
Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?

A. Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
B. Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
C. Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.
D. Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.
E. Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.

Answer: A,B

Explanation:
Options A and C are the most effective strategies. Incremental data loading (Option A) focuses on processing only the changed data, significantly reducing the processing time and resources used. Cloning and swapping (Option C) can provide a faster refresh while maintaining data availability (with a brief interruption during the swap). Option B, while faster than 'CREATE OR REPLACE TABLE, is still a full refresh and inefficient. Option D only mitigates the impact, not the underlying inefficiency. Option E will help improve performance but can be costly, should only be implemented for specific columns/tables and does not reduce the need for optimizing the data pipeline's refresh strategy directly.

NEW QUESTION # 258
A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
B. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
C. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
D. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
E. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.

Answer: D

Explanation:
Using an external table pointing to the S3 bucket is the most efficient and cost-effective approach. It allows you to query the data directly in S3 without ingesting it into Snowflake, saving on storage costs. Partitioning metadata further optimizes query performance by allowing Snowflake to only scan relevant partitions based on the query criteria.

NEW QUESTION # 259
You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

A. Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
B. Avoid large or small number and stick to the limited range of input values.
C. Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
D. Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
E. Cast input arguments and the result to 'FLOAT within the UDF.

Answer: C

Explanation:
Option B is the most reliable solution. Using a dedicated arbitrary-precision arithmetic library like 'Big.js' or 'Decimal.js' allows you to perform calculations with a higher degree of accuracy, overcoming JavaScript's inherent limitations in handling very large or very small numbers. Option A might help with formatting the output, but it doesn't address the precision issue during calculation. Option C and D will not solve the problem. Option E is not practical.

NEW QUESTION # 260
A data engineering team is implementing a data governance strategy in Snowflake. They need to track the lineage of a critical table 'SALES DATA' from source system ingestion to its final consumption in a dashboard. They have implemented masking policies on sensitive columns in 'SALES DATA. Which combination of Snowflake features and actions will MOST effectively allow them to monitor data lineage and object dependencies, including visibility into masking policies?

A. Use the INFORMATION_SCHEMA views like 'TABLES', 'COLUMNS', and 'POLICY_REFERENCES'. These views, combined with custom queries to analyze query history logs, will provide a complete lineage and masking policy overview.
B. Enable Account Usage views like 'QUERY_HISTORY, and 'ACCESS_HISTORY. These views directly show table dependencies and policy applications.
C. Create a custom metadata repository and use Snowflake Scripting to parse query history and object metadata periodically. Manually track dependencies and policy changes by analyzing the output.
D. Utilize Snowflake's Data Governance features, specifically enabling Data Lineage using Snowflake Horizon and utilize the view along with query the 'QUERY HISTORY view. These features natively track data flow and policy application.
E. Rely solely on a third-party data catalog tool that integrates with Snowflake's metadata API. These tools automatically track lineage and policy information and provide the best and most effective results.

Answer: D

Explanation:
Snowflake Horizon's Data Lineage feature is designed to track the flow of data through your Snowflake environment. Combining this with 'POLICY_REFERENCES (which shows which policies are applied to which objects) and (to see how data is transformed) provides the most complete and native solution. Account Usage views and INFORMATION_SCHEMA views provide valuable metadata, but don't offer lineage tracking out-of-the-box like Snowflake Horizon. While third-party tools and custom solutions are options, leveraging Snowflake's native capabilities is generally more efficient and cost-effective for basic lineage tracking.

NEW QUESTION # 261
......

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