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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q183-Q188):

NEW QUESTION # 183
You are designing a data pipeline in Snowflake that involves frequent updates to a staging table
`STG CUSTOMERS before merging the data into a production table `PROD CUSTOMERS. The
'DATA RETENTION_TIME parameter is set to 7 days at the account level. During a particular data load, a bug in the pipeline causes incorrect data to be loaded into `STG CUSTOMERS. You need to revert 'STG CUSTOMERS' to its state before the erroneous load. However, you also need to investigate the cause of the bug using the incorrect data in the current version of 'STG CUSTOMERS. What steps can you take to achieve both data recovery and root cause analysis effectively?

A. Set the 'DATA RETENTION_TIME IN DAYS parameter to 0 on 'STG_CUSTOMERS and perform a full table refresh from the source system.
B. Immediately drop and recreate the 'STG CUSTOMERS' table from a backup. Investigate the bug later by examining code logs.
C. Revert 'STG CUSTOMERS to its previous state using Time Travel. The incorrect data is unrecoverable as Time Travel replaces the current data. The bug will have to be found later.
D. Create a clone of 'STG_CUSTOMERS' before reverting it to its previous state using Time Travel.
Analyze the cloned table to identify the bug.

Answer: D

Explanation:
Creating a clone of 'STG_CUSTOMERS' before reverting it allows you to preserve the incorrect data for analysis while restoring the original table to its correct state using Time Travel. This allows for both data recovery and root cause analysis. Option A doesn't preserve the incorrect data for analysis. Option C loses the incorrect data, hindering debugging. Option D disables Time Travel and relies on a full refresh, which might be slow and complex.

NEW QUESTION # 184
Consider the following Snowflake warehouse definition: 'CREATE OR REPLACE WAREHOUSE MY WAREHOUSE WITH WAREHOUSE SIZE = 'MEDIUM' WAREHOUSE TYPE = 'STANDARD' AUTO SUSPEND = 300 AUTO RESUME = TRUE MIN CLUSTER_COUNT = 1 MAX CLUSTER_COUNT = 3 SCALING_POLICY = 'ECONOMY;' Which of the following statements are TRUE regarding this warehouse? (Select all that apply)

A. The 'WAREHOUSE_TYPE = 'STANDARD" setting is invalid and will cause an error.
B. The warehouse will automatically suspend after 5 minutes of inactivity.
C. The warehouse can scale up to a maximum of three clusters to handle concurrent queries.
D. The warehouse will always have at least one active cluster.
E. The warehouse is optimized for minimizing costs rather than maximizing performance when scaling.

Answer: B,C,D,E

Explanation:
The warehouse will auto-suspend after 300 seconds (5 minutes) of inactivity (A). = 1 ` ensures at least one active cluster (B). = 3' allows scaling to three clusters (C). 'SCALING_POLICY =
'ECONOMY" prioritizes cost savings over immediate performance during scaling (D).
'WAREHOUSE_TYPE = 'STANDARD'` is a valid setting, it defines the warehouse as using the standard model for resource management.

NEW QUESTION # 185
A company utilizes Snowflake's elastic compute capabilities for its daily batch processing jobs.
They have noticed that the warehouse consistently scales up to X-Large during the peak processing window (8 PM to 10 PM PST) and then scales back down. However, the cost of running the X-Large warehouse for these 2 hours is becoming significant. They are considering optimizing their compute costs. Which of the following strategies would be the MOST effective in reducing compute costs without significantly impacting the overall processing time of the batch jobs?

A. Analyze the SQL queries executed during the batch jobs and identify inefficient queries that consume excessive compute resources. Optimize these queries by rewriting them, adding appropriate indexes, or leveraging Snowflake's query optimization features.
B. Reduce the warehouse size to Large to decrease the hourly compute cost. While this will reduce costs, it will likely increase the overall processing time.
C. Enable auto-suspend for the warehouse with a shorter timeout period (e.g., 5 minutes). This will ensure the warehouse suspends quickly when idle, minimizing unnecessary compute usage.
D. Switch to using Snowflake's Serverless tasks for the batch processing jobs. Serverless tasks automatically manage compute resources and optimize costs based on actual usage.
E. Implement workload management rules to prioritize critical queries during the peak processing window. This can improve the efficiency of the warehouse and potentially reduce the need for scaling up to X-Large.

Answer: A,D

Explanation:
Optimizing SQL queries (Option D) is crucial for reducing compute costs. Efficient queries consume fewer resources, potentially reducing the need for a larger warehouse. Switching to Serverless tasks (Option E) is also highly effective, as Snowflake automatically manages compute resources and only charges for actual usage. Options A and B are less effective.
Reducing the warehouse size (A) will increase processing time, negating the cost savings. Auto- suspending (B) is good practice, but doesn't directly address the root cause. Prioritizing queries (C) is a good strategy but might not be as impactful as optimizing queries or using Serverless tasks.

NEW QUESTION # 186
You have created a custom role called `ANALYST ROLE in Snowflake. You want to grant this role the ability to create views in the 'REPORTING SCHEMA' schema of the 'ANALYTICS' database and query these views. Which set of privileges must be granted to the 'ANALYST ROLE'?

Answer: A

Explanation:
To create views, the 'CREATE VIEW privilege is required on the schema. To query the created views, the `SELECT privilege is needed. Using 'ON FUTURE VIEWS' ensures that the role automatically has 'SELECT privileges on any new views created in that schema. The USAGE' privilege is also required on the schema in order to access objects within that schema.

NEW QUESTION # 187
You are developing a Snowflake Notebook to perform data transformations using Snowpark. As part of the transformation, you need to filter a DataFrame based on a dynamically generated SQL expression. You have a Python dictionary 'filter_conditionS where keys are column names and values are the filter values. You want to construct a SQL 'WHERE clause from this dictionary and apply it to the DataFrame. However, the value types are mixed (strings, integers, dates). Which of the following approaches best handles the various data types and securely constructs the filter expression?

Answer: D

Explanation:
Option C is the best and most secure approach. It leverages Snowpark functions `col' and 'lit' to create the filter conditions. The `col' function represents a column in the DataFrame, and creates a literal value. This avoids SQL injection vulnerabilities and correctly handles different data types because Snowpark handles the data type conversion and quoting appropriately. Using `reduce" and "operator.and_' , we combine all conditions. Options A, B, D and E are all vulnerable to SQL injection because they directly construct the SQL string with user- provided values without proper escaping or parameterization. Additionally, options A, B, D and E have shortcomings for different date types.

NEW QUESTION # 188
......

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