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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested daily into Snowflake. The ingestion process currently uses a single 'X-Large' virtual warehouse. During peak hours, the data loading latency increases significantly, impacting downstream reporting. The team is considering either scaling up to a '3X-Large' warehouse or scaling out by creating a multi- cluster warehouse with a minimum of 2 and a maximum of 4 'X-Large' clusters. Which of the following factors should be prioritized when making this decision to optimize performance, considering cost and concurrency requirements?

A) The budget constraints and the higher per-second cost of a larger warehouse compared to the potential cost savings from reduced overall execution time.
B) The anticipated number of concurrent data loading jobs and the ability of Snowflake to automatically distribute these jobs across multiple clusters.
C) The complexity of the JSON data and the potential for improved parsing performance by a larger, single warehouse due to increased memory.
D) The type of JSON processing used. If using SQL functions like PARSE JSON, scaling out is more beneficial than scaling up.
E) The impact on other workloads sharing the same virtual warehouse, favoring scaling up to isolate the data loading process from other query activities.

2. 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) 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.
B) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
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) 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.
E) 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.

3. A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A) Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.
B) Drop the clustering key altogether to avoid re-clustering costs.
C) Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.
D) Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).
E) Increase the warehouse size to accelerate the re-clustering process.

4. You are managing a Snowflake environment where data retention is set to the default 1 day for all databases and tables. You need to clone a production table, 'CUSTOMER DATA, to a development environment to test some complex transformations. However, after cloning, you realize that the original 'CUSTOMER DATA' table in production was accidentally dropped 2 days ago. Which of the following statements accurately describe the situation and what can be done, if anything?

A) The cloned 'CUSTOMER DATA' table will only remain accessible if the source database was also cloned at the same time. If only the table was cloned, the cloned table is unusable after 1 day.
B) The cloned 'CUSTOMER DATA' table in the development environment will inherit the data retention period from the original 'CUSTOMER DATA' table, so it will also be inaccessible after 1 day if dropped in development.
C) The cloned 'CUSTOMER DATA' table in the development environment will remain accessible because the clone operation created a point-in-time snapshot.
D) While the source 'CUSTOMER DATA' is dropped and outside the retention period, the clone created will remain available and fully functional for querying. It is an independent copy. The clone is usable indefinitely, within the limits of your Snowflake account storage.
E) The cloned 'CUSTOMER DATA' table in the development environment will no longer be accessible because the source table has been dropped and the data retention period has expired. The clone is effectively useless.

5. A data engineer is implementing a data governance policy that requires masking PII data in non-production environments. They have identified a column 'CUSTOMER EMAIL' that needs to be masked. They want to use dynamic data masking in Snowflake, but the 'CUSTOMER EMAIL' column is referenced in several views. Which of the following approaches is MOST appropriate and avoids breaking the existing views?

A) Create masking policies on each of the individual views that reference the 'CUSTOMER EMAIL' column, using the same masking function.
B) Create a masking policy on the base table, but exclude the role used by the views from the policy's condition. This will prevent masking for those specific views.
C) Create a masking policy directly on the 'CUSTOMER EMAIL' column in the base table. This will automatically apply the masking to all views referencing the column.
D) Create a separate view that applies the masking function to the 'CUSTOMER EMAIL' column. Replace all existing views with the new masked view.
E) Create a masking policy on the base table but use a context function in the masking policy condition to check the database name. Mask the data only when the database name is the non-production database.

Solutions: