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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q113-Q118):

NEW QUESTION # 113
A data engineer needs to load JSON data containing customer profiles into a Snowflake table named 'CUSTOMER PROFILES'. Some JSON objects have missing fields, while others contain nested arrays. The target table `CUSTOMER PROFILES has columns: `customer id NUMBER, first _ name VARCHAR, last_name VARCHAR, address VARIANT'. Which of the following SQL statements is the MOST efficient and appropriate way to insert the data, handling potential missing fields without causing errors and allowing for future querying of the nested address data?

Answer: C

Explanation:
Option A is the most efficient because it directly loads the JSON data into the `address' column as a `VARIANT type, allowing for future querying of nested data without any data transformation during the load. It handles missing fields gracefully because Snowflake automatically handles missing fields in VARIANT columns. Option B tries to apply PARSE_JSON and COALESCE, but VARIANT handles nulls automatically. Option C unnecessarily attempts to construct a JSON object, which is less efficient and might miss fields. Option D filters based on customer_id, but might lose the rows when `address' is NULL. Option E parses the JSON after casting it to string.

NEW QUESTION # 114
A data engineering team is experiencing significant delays during their nightly ETL process in Snowflake. The process involves loading data from several external cloud storage locations (AWS S3, Azure Blob Storage) into a Snowflake table, transforming the data, and then loading it into multiple target tables. Monitoring shows the virtual warehouse CPU utilization is consistently at 100% during the peak ETL hours. Which of the following strategies would be MOST effective in reducing the ETL processing time and improving resource utilization?

A. Reduce the number of micro-partitions in the source data files by consolidating smaller files into larger ones.
B. Implement multi-clustering on the virtual warehouse, setting both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT to 2 or more.
C. Enable auto-suspend on the virtual warehouse to reduce credits consumed during idle time.
D. Increase the virtual warehouse size (e.g., from MEDIUM to LARGE) and monitor performance.
E. Repartition the Snowflake table into smaller micro-partitions to improve query performance.

Answer: B

Explanation:
Multi-clustering allows Snowflake to automatically scale out the virtual warehouse by adding more compute resources when the workload increases. This is the most effective way to handle high CPU utilization during peak ETL hours. Increasing the warehouse size (A) can help, but multi- clustering provides more dynamic scalability. Auto-suspend (B) doesn't address the performance issue. The micro- partition size of external source files (D) may impact initial load performance, but not the subsequent transformations and loading. Repartitioning the Snowflake table (E) may improve query performance, but not necessarily the ETL process itself.

NEW QUESTION # 115
You are using a Snowflake Notebook to perform data analysis on a large dataset. As part of your analysis, you need to create a custom Python function that calculates a complex metric based on multiple columns in a Snowflake table.
You want to apply this function to each row of the table and store the results in a new column.
Which of the following approaches is the MOST efficient and scalable way to achieve this using Snowflake and Python?

A. Load the entire Snowflake table into a Pandas DataFrame, apply the Python function to each row using 'DataFrame.apply()', and then write the modified DataFrame back to Snowflake.
B. Create a Snowflake Python User-Defined Function (UDF) that encapsulates the calculation logic and then use it in a 'SELECT statement to create a new column with the calculated values. Store the result in a new table using 'CREATE TABLE AS SELECT
C. Iterate over the rows of the Snowflake table using the Snowflake Connector for Python, call the Python function for each row, and then use "INSERT statements to insert the calculated values into a new table.
D. Create a stored procedure in Snowflake that runs the logic in a separate environment.
E. Use the '%%osql' magic command to execute a series of SQL UPDATE' statements that call the Python function using a IJDF.

Answer: B

Explanation:
Option C, creating a Snowflake Python IJDF and using it in a `SELECT statement within a
`CREATE TABLE AS SELECT statement, is the most efficient and scalable approach. Snowflake IJDFs allow you to execute Python code directly within the Snowflake engine, leveraging Snowflake's distributed processing capabilities. This avoids the overhead of transferring large amounts of data between Snowflake and the Python environment in the Notebook. Loading the entire table into a Pandas DataFrame (A) is not scalable for large datasets and can lead to memory issues. Using `%%osql' with `UPDATE statements (B) would be very slow due to the row-by-row updates. Iterating over rows using the Snowflake Connector (D) is also inefficient and not scalable. Option E is incorrect because it doesn't directly use Python code from the Notebook.

NEW QUESTION # 116
A data engineer is tasked with loading JSON data representing customer interactions into Snowflake. The JSON files contain varying schemas and nested arrays. To optimize query performance and minimize storage costs, which approach is MOST appropriate for handling the semi-structured data during loading, considering efficient data access patterns?

A. Use a CREATE VIEW statement to flatten and transform the VARIANT column into a relational structure for querying.
B. Utilize Snowflake's schema detection feature during loading to automatically create a relational table with appropriate data types for the JSON data.
C. Create a relational schema based on the most common JSON structure and load only those fields into corresponding columns, discarding any other data.
D. Parse and pre-process the JSON data outside Snowflake to create a consistent relational structure before loading.
E. Load the JSON data directly into a VARIANT column without any transformations and use LATERAL FLATTEN to extract data during querying.

Answer: B

Explanation:
Snowflake's schema detection during loading automatically creates a relational table based on the JSON data's structure, assigning appropriate data types. This avoids the overhead of manual schema definition and data transformation. While VARIANT can be used initially, schema detection provides a structured approach for querying semi-structured data. Choosing a relational schema upfront and discarding extra fields (B) leads to data loss. Using a view on a VARIANT column adds query overhead. Pre-processing outside Snowflake adds complexity.

NEW QUESTION # 117
A security architect is designing a role hierarchy in Snowflake for a data analytics team. They need to grant specific privileges to different user groups. 'DATA ENGINEER' role should have the ability to create and manage databases. 'DATA ANALYST' role should be able to query data from those databases. 'REPORT USER' role needs read-only access to specific views. The architect wants to ensure minimum privilege and enforce role separation. Which of the following sequences of SQL commands would correctly establish this role hierarchy and grant necessary privileges?

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

Answer: B

Explanation:
Option E is the most complete answer. It creates the roles, grants necessary database creation privileges to DATA_ENGINEER, allows DATA ANALYST to use and query the database, and REPORT USER to use the database and select from the specified view. The role grants establish the hierarchy. Options A and B have incorrect grant statements for select on all tables in the database and lack the creation of Roles at the begining. Option D does not grant the roles to each other, leaving the hierarchy incomplete, and incorrectly attempts to grant select on future tables directly to a view.

NEW QUESTION # 118
......

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SOL-C01 Latest Test Format & Training SOL-C01 Tools

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q113-Q118):

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