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NEW QUESTION # 84
What are Snowflake notebooks primarily used for?
Answer: C
Explanation:
Snowflake Notebooks provide an interactive environment where developers and analysts can combineSQL, Python, visualization libraries, and documentation. Use cases include exploratory analysis, ETL prototyping, data engineering workflows, machine learning model development, and dashboard creation using Streamlit components.
Notebooks allow mixing of SQL and Python cells with shared session state, enabling smooth transitions between Snowpark DataFrames, SQL queries, visual charts, and markdown explanations. They enhance reproducibility and collaboration, supporting versioning, parameterization, and seamless Snowflake compute integration.
Incorrect options:
* Raw data is stored in Snowflake tables, not in notebooks.
* User authentication is handled by the Cloud Services Layer.
* Managing cloud storage is automatic and not a notebook responsibility.
Thus, Snowflake notebooks are an end-to-end development and analytics interface.
NEW QUESTION # 85
A Snowflake account uses separate virtual warehouses for different workloads: 'REPORTING WI-
4' for reporting queries and
'TRANSFORM WH' for data transformation jobs. A user, Alice, is responsible for running both types of workloads. Alice is experiencing performance issues with reporting queries when the transformation jobs are running concurrently. Both warehouses are currently configured with the
'STANDARD' size. What are the recommended strategies to improve the performance of reporting queries without significantly impacting the performance of transformation jobs?
Answer: D
Explanation:
Enabling a multi-cluster warehouse for 'REPORTING_WH' with a 'STANDARD' scaling policy allows Snowflake to automatically scale up the number of clusters to handle the increased workload from concurrent reporting queries. The Query Acceleration Service (QAS) will try to reduce the resource consumption of reporting queries. Implementing Resource Monitors for
`TRANSFORM WHS will prevent it from consuming excessive credits and impacting the performance of 'REPORTING_WH' during concurrent execution.
NEW QUESTION # 86
A data engineering team is loading data from a CSV file containing customer information into a Snowflake table. The CSV file occasionally contains malformed data, such as missing fields or fields with incorrect data types (e.g., a string in a numeric field). The team wants to ensure that the loading process continues even when such errors occur, and that the problematic records are tracked for later analysis. Which of the following COPY INTO options should be used to achieve this, and how should the error be handled?
Answer: C
Explanation:
'ON_ERROR = 'CONTINUE'` ensures that the load continues despite errors.
'VALIDATION_MODE = RETURN_ERRORS' allows you to retrieve details about the errors using the RESULT SCAN' function, which can then be stored in a separate error table. This allows for later analysis of problematic records. = FALSE is necessary if the column count varies between input records.
NEW QUESTION # 87
Which task is performed in the Cloud Services layer in the Snowflake architecture?
Answer: B
NEW QUESTION # 88
What is the primary purpose of the COPY INTO command in Snowflake for data loading?
Answer: B
Explanation:
COPY INTO <table> bulk-loads data from internal or external stages into Snowflake tables. It supports transformations, validation, and multiple file formats.
It does not replicate databases, manage roles, or create warehouses.
NEW QUESTION # 89
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