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NEW QUESTION # 51
What information can be accessed using the Snowsight Monitoring tab?
Answer: C
Explanation:
The Snowsight Monitoring tab provides a centralized view of virtual warehouse usage metrics, enabling administrators and developers to evaluate how compute resources are being consumed. This includes critical insights such as credit usage, query load, concurrency levels, average queue times, execution durations, and auto-scaling activity (for multi-cluster warehouses). These metrics help determine whether a warehouse is correctly sized, whether concurrency issues are occurring, or whether workloads require scaling up or adding clusters.
Query history is available in a different section-"Activity # Query History"-not under Monitoring. Time Travel snapshots are not visualized within Monitoring; Time Travel is controlled via retention parameters and accessed with SQL (AT/BEFORE clauses). Schema change history is also not part of Monitoring and instead is discoverable through ACCOUNT_USAGE or specific metadata views.
The Monitoring tab exists specifically to help evaluate warehouse performance and resource consumption, enabling optimization of compute spending and better workload management.
NEW QUESTION # 52
What information is available when previewing a table in Snowsight?
Answer: B
Explanation:
When you preview a table in Snowsight, Snowflake returns up to the first 100 rows of the table's data. This preview is designed for quick inspection of the table's contents and structure without requiring the user to manually write a SELECT query. The preview interface allows basic exploration such as scrolling, simple sorting, and viewing column metadata.
Tables are not "stored" in a specific virtual warehouse; warehouses provide compute only. The table definition (DDL) can be viewed in the Table Details section but is not the main output of the Preview action.
Snowsight does not natively generate an Entity Relationship (ER) diagram for the table as part of the preview.
NEW QUESTION # 53
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: A
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 # 54
Which of the following statements are true about Cortex functions?
Answer: A,B,C
Explanation:
SnowflakeCortex functionsenable AI-powered operations directly within SQL and Python, supporting multiple languages via translation, classification, summarization, and document extraction functions. They operate seamlessly onstructuredandsemi-structureddata stored in VARIANT columns, allowing LLMs to process text, JSON, documents, and rich content.
Cortex functions can be combined within SQL pipelines-for example, using PARSE_DOCUMENT to extract data, followed by CLASSIFY_TEXT to categorize extracted fields, and then SUMMARIZE to produce narrative summaries. This composability enables sophisticated multi-stage workflows for document intelligence, support ticket analysis, compliance automation, and semantic enrichment.
They arenot limited to a single domain; Cortex functions apply to finance, retail, support operations, healthcare, legal analysis, and general AI-assisted data processing.
Thus, Cortex functions are versatile, multi-domain, multi-language tools integrated with Snowflake's compute and governance layers.
NEW QUESTION # 55
A security auditor discovers that multiple users are using the 'ACCOUNTADMIN' role directly for their daily tasks. This violates the principle of least privilege. Which of the following actions should you take to remediate this situation? (Choose two)
Answer: B,C
Explanation:
Options A and B are the most appropriate actions. A directly addresses the principle of least privilege by creating custom roles with specific privileges and revoking the 'ACCOUNTADMIN' role. Option B enhances the security posture by implementing MFA, reducing the risk of unauthorized access to the `ACCOUNTADMIN' role. Option C is a weak mitigation. Option D is not the correct approach, as 'SECURITYADMIN' still has broad privileges. E doesn't address the problem of over-privileging.
NEW QUESTION # 56
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