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Valid DEA-C02 Exam Cram | Latest DEA-C02 Learning Material

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q297-Q302):

NEW QUESTION # 297
Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A. Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
B. Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
C. Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
D. Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
E. Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.

Answer: D

Explanation:
Clustering on '(EVENT _ TIMESTAMP, APPLICATION_ID)' directly addresses the common query patterns. Regularly running 'OPTIMIZE TABLE EVENT DATA' with an appropriately sized warehouse ensures the data remains well-clustered as new data is added, reducing clustering depth and maintaining performance. Monitoring clustering depth is essential to identify when reclustering is needed. Clustering on a single dimension like 'USER IDS (C) doesn't address the primary query patterns. Creating separate tables (A, D) introduces complexity and management overhead. Materialized views (E) are helpful for specific pre-aggregated results, but clustering optimizes the base table for a wider range of queries. Optimizing with the right sized warehouse is crucial, a small warehouse might take an extremely long time.

NEW QUESTION # 298
You are tasked with designing a data sharing solution where data from multiple tables residing in different databases within the same Snowflake account needs to be combined into a single view that is then shared with a consumer account. The view must also implement row-level security based on the consumer's role. Which of the following options represent valid approaches for implementing this solution? Select all that apply.

A. Create a secure view that joins tables from different databases and implement row-level security using a row access policy based on the CURRENT ROLE() function. Masking policy cannot provide role based access control so will not work.
B. Create a standard view that joins tables from different databases using aliases and implement row-level security using a UDF that checks the consumer's role and filters the data accordingly.
C. Create a standard view with a stored procedure to handle the joins across databases and use EXECUTE AS OWNER to avoid permission issues. This standard view should be shared.
D. Create a secure view that joins tables from different databases using fully qualified names (e.g., 'DATABASEI .SCHEMAI . TABLET) and implement row-level security using a masking policy based on the CURRENT_ROLE() function.
E. Create a view for each table and then build a final view using 'UNION ALL' to combine data from all the views and implement row-level security with a role based row access policy. Standard views should not be used in data sharing.

Answer: A,D

Explanation:
Options A and C are the valid approaches. A secure view is essential for data sharing. Fully qualified names are required to reference objects across databases. Row-level security can be implemented using either a row access policy or a masking policy (with some limitations). Option A incorrectly mentions a masking policy for row level access control. Option C is correct as row access policies are designed for that case. Option D create standard views and sharing standard views is not a good practice. Stored procedures cannot be used in the definition of a view for data sharing, so E is invalid. B is also invalid as standard view.

NEW QUESTION # 299
You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

A. When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.
B. Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
C. Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
D. Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.
E. Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.

Answer: A

Explanation:
The best solution for idempotent processing is option C, using a MERGE statement with a staging table and timestamp-based logic. This allows you to load all changes into a staging area, then merge only the latest version (based on 'timestamp' ) into the target table, effectively overwriting older versions. Option A, using a regular task and MERGE, can work, but it doesn't inherently address idempotency without the staging table and timestamp logic. Option B, using a materialized view, can work but does not capture all changes from the original table. Option D can work, but it doesnt scale well. Option E is incorrect; streams do not support unique key constraints to prevent capturing duplicate updates.

NEW QUESTION # 300
You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

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

Answer: D

Explanation:
Option E correctly parses the JSON, iterates through the nested arrays ('data' and 'items'), safely checks for the existence of 'items' and 'value' properties, and extracts the 'value' from each object, joining them into a comma-separated string. This option also gracefully handles the case where 'items' or 'value' might be missing. Options A, B and C are close, but can raise error if data element is missing. Option D returns an array.

NEW QUESTION # 301
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
B. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
C. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
D. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
E. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.

Answer: C

Explanation:
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention

NEW QUESTION # 302
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