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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q251-Q256):

NEW QUESTION # 251
You have enabled directory tables on an external stage pointing to an AWS S3 bucket. The S3 bucket contains millions of small JSON files. When you query the directory table, you notice that the query performance is slow, even when filtering by 'METADATA$FILENAME'. What strategies can you implement to optimize the performance of querying the directory table?

A. Periodically refresh the directory table using 'ALTER STAGE REFRESH;'.
B. Create a materialized view on top of the directory table, including the 'METADATA$FILENAME , , and any other frequently filtered columns.
C. Increase the virtual warehouse size used for querying the directory table.
D. Reduce the number of files in the S3 bucket. Directory tables perform better with fewer files.
E. Create an index on the 'METADATA$FILENAME column of the directory table.

Answer: B

Explanation:
Creating a materialized view (Option B) is the most effective way to optimize the performance of querying a directory table. Materialized views pre-compute and store the results of a query, allowing for faster retrieval of data, especially when the underlying table (the directory table in this case) is large. Increasing warehouse size (Option A) can help to a certain extent, but a materialized view provides a more significant performance boost. Refreshing the directory table (Option C) keeps the metadata up- to-date but doesn't directly improve query performance.
Creating an index (Option D) is not possible on directory tables. While reducing the number of files (Option E) might help slightly, it's often impractical and doesn't address the underlying issue of querying a large dataset. It's best to handle millions of files with proper indexing and data partitioning.

NEW QUESTION # 252
A team is using Snowsight for developing and testing SQL queries. They want to ensure that all queries executed by a specific user, analystl', are automatically tagged with a custom tag
'team:analyticS. Which of the following methods, leveraging Snowsight or Snowflake features, achieves this goal with minimal administrative overhead? Select all that apply.

A. Create a Snowflake resource monitor that automatically applies the `team:analytics' tag to all queries executed by `analystl ` .
s code auto-completion to auto-append ALTER SESSION SET QUERY_TAG =
B. Configure the QUERY _ TAG session parameter for each Snowsight worksheet used by `analystl
C. Use Snowsight
team:analytics at the start of every worksheet.
D. There is no method to enforce automatic tagging of queries in Snowsight or Snowflake without manually adding the tag to each query.
E. Set the QUERY_TAG parameter at the user level using 'ALTER USER analystl SET QUERY_TAG
= 'team:analytics';'.

Answer: E

Explanation:
Setting the QUERY TAG parameter at the user level (Option D) is the most efficient and centralized way to ensure all queries executed by `analystl are automatically tagged. This avoids the need for individual worksheet configuration. Option A requires manual configuration for each worksheet, which is not ideal. Option B Resource Monitors do not have tagging capabilities.
Option C also requires manual updates to each worksheet. Option E is incorrect, as user-level parameter setting provides the desired functionality.

NEW QUESTION # 253
You are inserting data into a table named 'EVENTS' which contains a column 'EVENT DATA' of type 'VARIANT'. The data being inserted comes from an external stage and is in JSON format.
However, you are encountering errors because some of the JSON documents contain deeply nested arrays and objects, leading to excessive memory consumption during the INSERT operation. Which of the following actions can you take to mitigate this issue and successfully load the data using the 'INSERT' command with data from a stage? Choose TWO.

A. Use the `VALIDATE function to identify problematic JSON documents and exclude them from the INSERT operation.
B. Break down the INSERT operation into smaller batches, processing subsets of the data at a time.
C. Pre-process the JSON data outside of Snowflake to flatten or simplify the nested structures before loading it into the stage.
D. Increase the size of the virtual warehouse to provide more memory for the INSERT operation.
E. Use = TRUE file format option for the stage.

Answer: B,C

Explanation:
Options B and E are the most effective solutions. Option B, pre-processing the data to flatten it reduces the complexity of individual JSON documents, lowering memory consumption during the insert. Option E, batching the INSERT operations limits the amount of memory used in each individual operation, preventing the warehouse from being ovemhelmed. Option A, increasing the warehouse size can help, but it's often a more expensive solution than optimizing the data loading process itself. Option C only strips the outer array. Option D does not resolve the issue during load.

NEW QUESTION # 254
You are tasked with loading data into a Snowflake table named 'ORDERS' from a CSV file stored in an internal stage called `my_stage'. The CSV file, 'orders data.csv' , contains a header row, and the fields are comma-separated. Some of the columns in the CSV file are enclosed in double quotes. The table `ORDERS' has the following structure: `order id INTEGER, customer id INTEGER, order_date DATE, total _ amount DECIMAL(10, 2), order _ status VARCHAR(20)'.
Which of the following 'COPY INTO' statements would correctly load the data into the 'ORDERS' table, assuming that the CSV columns map directly to the table columns in the order they appear?

Answer: E

Explanation:
Option B is the correct answer because it specifies the CSV file type, skips the header row, uses a comma as the field delimiter, and specifies that fields are optionally enclosed by double quotes.
This is crucial for correctly parsing the CSV dataERROR_ON_COLUMN_COUNT_MlSMATCH = FALSE, is needed if the CSV has extra columns.

NEW QUESTION # 255
You are designing a data pipeline in Snowflake where raw data is ingested into a staging schema, transformed, and then loaded into a production schema. You want to ensure that only authorized users can access the data in the production schema, and that data analysts can only query the data but not modify it. You have two roles: `DATA ENGINEER (responsible for loading and transforming data) and 'DATA ANALYST (responsible for querying data). Which of the following sets of grants provide the MOST secure and appropriate access control for this scenario? Assume that 'PRODUCTION DB' and 'STAGING DB' databases already exist.

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

Answer: B

Explanation:
Option D provides the most secure and appropriate access control. It uses `GRANT SELECT ON FUTURE TABLES' for DATA ANALYST ensuring they automatically have access to new tables without needing explicit grants. It also uses `GRANT INSERT, UPDATE, DELETE ON FUTURE TABLES' for `DATA_ENGINEER so they are able to load and transform data to any new table within Option A only grants select on current tables. Option B grants insert, update and delete on all current tables which is not ideal. Option C grants ALL privileges which violates principle of least privilege. Option E does not grand any rights to schema for DATA_ANALYST , it will not work. Also, does not cover future grants.

NEW QUESTION # 256
......

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