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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Topic 2: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Topic 4: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 6: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 7: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
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질문 # 36
You have a table named 'ORDERS with a 'ORDER DATE column stored as VARCHAR. The column contains dates in the format 'YYYY-MM-DD HH24:Ml:SS.FF3'. You need to calculate the number of orders placed in January 2023. Which of the following SQL statements will achieve this most efficiently and accurately, considering potential time zone differences configured at the user or account level?
정답：E
설명：
Option E is the most robust because it explicitly handles the VARCHAR to TIMESTAMP conversion, incorporates timezone conversion from UTC to the session's timezone, and then extracts the DATE part for accurate filtering. This accounts for potential timezone discrepancies that might affect date boundaries. Options A and B rely on string manipulation, which is prone to errors and not recommended for date operations. Option C lacks time zone considerations, which can lead to issues. Option D is less efficient because MONTH() and YEAR() function needs to be applied, and it also lacks timezone conversion consideration.
질문 # 37
In Snowflake, how do window functions differ from table functions?
정답：D
설명：
Table functions operate on windowed rows, distinguishing them from window functions in Snowflake.
질문 # 38
A Data Analyst created a cost overview dashboard in Snowsight. Management has asked for a system date filter to easily change the time period and refresh the data in all dashboard tiles with a single filter selection.
The system date filter is shown below:
The Analyst wants to apply the filter onto individual dashboard components.
Adding which where clause to the queries will apply the filter as required?
정답：A
설명：
In Snowsight, the modern web interface for Snowflake, System Filters are specialized keywords that provide out-of-the-box interactivity for dashboards and worksheets. These filters allow non-technical users to manipulate the timeframes of visualizations (e.g., switching from "Last 7 days" to "Last 12 months") without requiring an analyst to manually rewrite the underlying SQL code.
The most critical keyword for temporal filtering is :daterange. When a dashboard contains a date filter widget (as shown in the provided exhibit), the :daterange keyword acts as a dynamic placeholder for the range selected by the user. Unlike a standard variable that might represent a single date, :daterange is specifically designed to handle the start and end boundaries of a period. When injected into a WHERE clause, Snowflake automatically expands this keyword into the appropriate logic to filter records between those two points in time.
Evaluating the Options:
* Options A and B are incorrect because they use hard-coded logic (-7 days). While these would return data for the last week, they are static. Changing the filter in the dashboard UI would have no effect on these queries, failing the requirement to "easily change the time period" via the filter selection.
* Option C is incorrect because :date_filter is not a reserved system keyword in Snowsight. While an analyst could create a custom filter with that name, it would not automatically link to the standard system date widget shown in the image.
* Option D is the 100% correct answer. Using WHERE <column> = :daterange is the documented standard for linking a query to the Snowsight system date filter. This ensures that every tile in the dashboard remains synchronized with the user's global date selection, providing a cohesive and interactive reporting experience.
질문 # 39
In data presentations for business use analyses, what significance do identifying patterns and trends hold?
정답：B
설명：
Identifying patterns and trends aids in insightful analyses in business use scenarios.
질문 # 40
You are tasked with creating a data pipeline that ingests data from various sources, including a Snowflake Marketplace data share, and prepares it for analysis. The pipeline involves several transformations and enrichments. Which of the following methods offer the BEST approach to manage data lineage and auditability within this pipeline, considering the shared data from the Marketplace?
정답：C
설명：
Option C is the best approach. Snowflake's built-in functions like 'SYSTEM$GET_PREDECESSORS' and allow you to programmatically trace the dependencies and data flow within your Snowflake environment, including data accessed from shares. Combining this information with a metadata repository provides a robust and auditable data lineage solution. Option A is insufficient as it doesn't provide a structured and easily navigable lineage. Option B is viable but requires significant manual effort to maintain and scale. Option D creates unnecessary storage overhead and doesn't inherently improve data lineage tracking. Option E is not recommended as replicating shared data goes against the purpose of data sharing and can lead to synchronization issues.
질문 # 41
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