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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q63-Q68):

NEW QUESTION # 63
A Snowflake Notebook is configured to run a series of data processing steps on a schedule using the Snowflake Task feature. One of the steps involves calling an external API to enrich the data.
Due to network instability, the API calls occasionally fail, causing the entire task to fail. You want to implement error handling within the notebook to retry failed API calls up to a certain number of times before giving up. Assuming you are using Python within the Snowflake Notebook and Snowpark, how would you best implement this retry mechanism while ensuring minimal disruption to the notebook's workflow and preserving error information?

A. Use the `snowflake.snowpark.functions.call_udf function with the 'retry_count' parameter set to the desired number of retries. This will automatically handle retries for IJDF calls within the notebook.
B. Use a dedicated Python library like 'tenacity' within the Snowflake Notebook. Configure `tenacity' to retry the API call with exponential backoff and custom exception handling. Log all retry attempts and final error messages.
C. Utilize the Snowflake Task's built-in error handling capabilities to automatically retry the task upon failure. Configure the task to retry a specific number of times with a specified delay between retries.
D. Wrap the API call within a 'try...except' block. Inside the `except' block, use `time.sleep()' to introduce a delay, and then recursively call the function containing the API call until the maximum number of retries is reached. If all retries fail, log the error and re-raise the exception.
E. Implement a custom decorator function that handles retries. The decorator should catch exceptions from the API call, log the error, introduce a delay, and retry the API call until the maximum number of attempts is reached. Apply this decorator to the function containing the API call.

Answer: B

Explanation:
Using a dedicated library like `tenacity' (E) provides the most robust and flexible retry mechanism.
It offers features like exponential backoff, customizable retry strategies, and detailed logging.
While `try...except' blocks (A) can work, they are less elegant and harder to maintain for complex retry logic. Snowflake does not have direct error handling mechanisms for external API calls (C, D) within notebooks. A decorator function (B) can work, but `tenacity' provides more features with less code.

NEW QUESTION # 64
What is a key feature of Snowflake notebooks?

A. Text-only data display
B. Integrated environment for writing queries and visualizing results
C. Manual hardware configuration
D. Command-line interface only

Answer: B

Explanation:
Snowflake Notebooks provide an integrated, interactive development environment that enables users to write SQL and Python code, visualize results, and document analysis-all in the same workspace. They support data visualizations, charts, tables, and Streamlit-based interactions. Notebooks run entirely within Snowsight and require no manual hardware configuration. They are not CLI-based and support much richer displays than plain text. This integration streamlines the entire data exploration, transformation, and modeling workflow.

NEW QUESTION # 65
You are tasked with deploying a new data application to Snowflake. This application requires several schemas for staging, transformation, and reporting. What is the recommended approach to create these schemas using Infrastructure as Code (laC) and ensuring consistency across multiple Snowflake environments (DEV, TEST, PROD)?

A. Utilize a third-party lac tool (e.g., Terraform) to define the schemas as resources, managing their creation and updates across environments.
B. Copy the DDL (Data Definition Language) scripts generated by the Snowflake UI after manually creating schemas in one environment and executing them in others.
C. Manually create the schemas in each environment using the Snowflake web UI, documenting the steps for repeatability.
D. Use a Snowflake client (e.g., SnowSQL) and a script to execute 'CREATE SCHEMA' statements, parameterizing the environment-specific database and schema names.
E. Utilize Snowflake's data replication feature to replicate schemas between different environments

Answer: A

Explanation:
Using a dedicated lac tool like Terraform is the best practice for managing Snowflake resources, including schemas. It ensures consistency, repeatability, and version control. Options A and D are manual and error-prone. Option B is better than A and D but lacks the full features of lac tools (state management, dependency resolution, etc.). Option E is for data replication and not schema creation.

NEW QUESTION # 66
When a virtual warehouse is scaled up (e.g., from X-Small to Small), what is the primary impact?

A. It reduces the cost of running queries.
B. It can process larger and more complex queries faster.
C. It allows more concurrent users to connect.
D. It gains more storage capacity.

Answer: B

Explanation:
Scaling up a warehouse increases compute resources (CPU, memory), improving performance on heavy workloads: large joins, transformations, and long-running analytical queries.
It does not increase storage, reduce cost, or guarantee higher concurrency (multi-cluster handles concurrency).

NEW QUESTION # 67
A Snowflake administrator notices that a virtual warehouse named 'REPORTING WH' is frequently idle during off-peak hours. They want to optimize costs by ensuring the warehouse suspends automatically when not in use, but they also need to minimize the latency for the first query after a period of inactivity. Which combination of actions would achieve this goal?

A. Set 'AUTO SUSPEND to a low value (e.g., 60 seconds) and set 'AUTO RESUME to FALSE.
B. Set 'AUTO SUSPEND to NULL and set 'AUTO RESUME to FALSE.
C. Set 'AUTO SUSPEND to a high value (e.g., 3600 seconds) and set `AUTO RESUME to TRUE.
Also, Enable Result Cache.
D. Set 'AUTO SUSPEND to a low value (e.g., 60 seconds), ensure `AUTO RESUME is TRUE, and ensure commonly used tables and data are clustered correctly to minimize initial query scan time.
E. Set 'AUTO_SUSPEND' to a low value (e.g., 60 seconds) and ensure AUTO_RESUME is TRUE.

Answer: D,E

Explanation:
Setting `AUTO SUSPEND to a low value (60 seconds) ensures the warehouse suspends quickly when idle (cost optimization). 'AUTO_RESUME = FALSE (A) would require manual warehouse resumption, increasing latency. Setting 'AUTO_RESUME' to TRUE (B, C, E) allows automatic resumption upon a new query. 'AUTO SUSPEND to a high value(C) defeats the purpose of cost optimization. Setting 'AUTO_SUSPEND' to NULL(D) prevents suspension, negating cost savings.
Reducing latency requires both automatic resumption (AUTO RESIJME = TRUE) AND minimizing the initial query scan time through proper clustering (E), in combination with AUTO RESUME and a low AUTO_SUSPEND time. Result Cache only helps on repeat queries with the same parameters.

NEW QUESTION # 68
......

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Katie Martin Katie Martin

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