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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q82-Q87):

NEW QUESTION # 82
You're using Snowpipe Streaming to ingest JSON data into a Snowflake table. The JSON data contains nested objects and arrays. You're encountering errors related to data type mismatches during ingestion. The target table schema is defined with specific data types for each column. Which of the following approaches is MOST effective for handling this data type mismatch issue within the Snowpipe Streaming context, considering minimal transformation?

A. Adjust the auto_ingest property of the Snowpipe object to force data type conversion.
B. Use a variant column in the Snowflake table and perform data type casting during querying.
C. Use a COPY INTO statement with a transformation function to cast the data during ingestion.
D. Modify the client-side application to cast the data to the correct data type before sending it to Snowpipe Streaming.
E. Create a Stored Procedure in Snowflake to transform the data after it has been ingested using Snowpipe Streaming.

Answer: D

Explanation:
B is the most effective approach. Snowpipe Streaming is designed for low-latency ingestion with minimal transformation at ingestion time. Data type mismatches are best resolved at the source (client-side) before the data is sent to Snowflake. A introduces complexity at query time and isn't ideal for a fixed schema. C adds latency and post-ingestion processing. D is not applicable to Snowpipe Streaming; COPY INTO is for Classic Snowpipe. E, is not related to data type conversion.

NEW QUESTION # 83
You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

Answer: E

Explanation:
Option E is the most efficient approach, as it calculates the average performance rating per department first and then joins the original DataFrame to only include departments with an average rating above 4.0 using 'left join'. The correct department and salary calculations, filter to only the top 3 with high performace review.

NEW QUESTION # 84
You have a directory table 'my_directory_table' pointing to a stage containing CSV files with headers. You need to query the directory table to find all files modified in the last 24 hours and load those CSV files using COPY INTO into a target table Assume the target table exists and has appropriate schema'. Which of the following SQL statements, or set of statements, will accomplish this efficiently? Note: Consider efficient file loading.

Answer: A

Explanation:
Option E is the most efficient way to load the files using 'COPY INTO' and the 'FILES' parameter. The 'FILES' parameter accepts a list of filenames to load. Option D is incorrect because the 'PATTERN' parameter expects a regular expression, not a concatenation of filenames. Options A, B, and C require more complex processing. Using subquery expression in the copy command is the best approach for filtering files based on directory table data.

NEW QUESTION # 85
Your team is developing a set of complex analytical queries in Snowflake that involve multiple joins, window functions, and aggregations on a large table called 'TRANSACTIONS. These queries are used to generate daily reports. The query execution times are unacceptably high, and you need to optimize them using caching techniques. You have identified that the intermediate results of certain subqueries are repeatedly used across different reports, but they are not explicitly cached. Given the following options, which combination of strategies would MOST effectively utilize Snowflake's caching capabilities to optimize these analytical queries and improve report generation time?

A. Use temporary tables to store the intermediate results of the subqueries. These tables will be automatically cached by Snowflake and can be reused by subsequent queries within the same session.
B. Create common table expressions (CTEs) for the subqueries and reference them in the main query. CTEs will force Snowflake to cache the results of the subqueries, improving performance.
C. Create materialized views that pre-compute the intermediate results of the subqueries. This will allow Snowflake to automatically refresh the materialized views when the underlying data changes and serve the results directly from the cache.
D. Consider using 'CACHE RESULT for particularly expensive subqueries or views. This is a hint to snowflake to prioritize caching the result set for future calls.
E. Utilize the "RESULT_SCAN' function in conjunction with the query ID of the initial subquery execution to explicitly cache and reuse the results in subsequent queries. This approach requires careful management of query IDs.

Answer: C,D

Explanation:
Creating materialized views (D) for the intermediate results is the most effective approach, as Snowflake automatically manages the refresh and caching. 'CACHE RESULT (E) Provides a way to explicitly cache the results. Temporary tables (A) are session-specific and not suitable for persistent caching across reports. CTEs (B) do not guarantee caching and are primarily for query readability. 'RESULT SCAN' (C) is complex to manage and requires manual tracking of query IDs. Therefore, a combination of materialized views and CACHE RESULT will provide the best caching strategy.

NEW QUESTION # 86
You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A. Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
B. External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
C. Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
D. Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
E. All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.

Answer: A,B,E

Explanation:
Cloning a schema clones all objects within it (tables, views, UDFs, etc.). Cloning uses metadata cloning initially, so it doesn't immediately duplicate the data. Sequences resume from where they left off (if not used cloned schema). Tasks and streams are not cloned when cloning a schema, unless the clone is done at the database level. External tables are cloned, but the data files remain in their original cloud storage location.

NEW QUESTION # 87
......

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