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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: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
| Topic 3: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Topic 4: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 6: Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 7: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
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NEW QUESTION # 46
A Data Analyst needs to temporarily hide a tile in a dashboard. The data will need to be available in the future, and additional data may be added. Which tile should be used?
Answer: B
Explanation:
In Snowsight, managing dashboard layouts requires an understanding of how tiles (queries or visualizations) are stored versus how they are displayed. When an analyst wants to remove a tile from the visible dashboard grid without destroying the underlying query logic or historical configuration, the Unplace action is the correct functional choice.
When a tile is unplaced, it is removed from the dashboard's active layout but remains part of the dashboard's
"library" of available content. This is a critical distinction from the Delete action (Option C), which permanently removes the tile and its associated SQL code from the dashboard object. Unplacing allows the analyst to "archive" the work temporarily. Because the tile still technically exists within the dashboard's metadata, any new data added to the underlying tables will still be processed by the query whenever the tile is eventually placed back onto the grid.
Evaluating the Options:
* Option A (Show/Hide) is not a standard standalone command for dashboard tile management in Snowsight; visibility is typically managed through placement on the grid.
* Option B (Duplicate) creates a second copy of the tile. While this preserves the data, it does not satisfy the requirement to "hide" the current tile; it actually adds more clutter to the dashboard.
* Option C (Delete) is incorrect because the prompt specifies that the data and tile will need to be available in the future. Deleting would require the analyst to rewrite the SQL and reconfigure the visualization from scratch.
* Option D is the 100% correct answer. Unplacing is the "soft-remove" feature of Snowsight. It preserves the tile in the "Unplaced Tiles" sidebar, allowing for quick restoration at a later date. This feature is essential for analysts who need to manage evolving reporting requirements where certain metrics may only be relevant seasonally or during specific business cycles.
NEW QUESTION # 47
You are building a real-time dashboard to monitor website traffic and user behavior for an e-commerce company. The data includes page views, clicks, add-to-carts, and purchases, streamed continuously into Snowflake. You need to visualize the conversion funnel (page views -> clicks -> add-to-carts -> purchases) in real-time and identify drop-off points. Given the following table schema: "'sql CREATE OR REPLACE TABLE website_events ( event_timestamp TIMESTAMP NTZ, event_type VARCHAR(50), user_id VARCHAR(IOO), page_url VARCHAR(255) ); Which approach, including code snippets, would be the MOST efficient and scalable way to achieve this real-time conversion funnel visualization, taking into account the high volume of streaming data?
Answer: D
Explanation:
Option C is the most efficient and scalable. A Snowflake Stream allows you to track changes to the 'website_events' table in real- time. A Snowpipe enables continuous data ingestion. A materialized view pre-calculates the conversion funnel metrics, significantly improving query performance compared to querying the base table directly, especially with high data volumes. Connecting a real-time dashboarding tool to the materialized view provides a real-time view of the funnel. Option A involves periodic querying, which is less real-time and less efficient. Option B suggests direct connection with a BI tool without pre-aggregating the Data, resulting into dashboard performance issue. Option D introduces unnecessary complexity with external message queues and stream processing frameworks. Exporting data to Python dataframe is not scalable for large data volumes.
NEW QUESTION # 48
A data analyst is implementing a data preparation pipeline using Snowflake stored procedures to cleanse and transform data,. During testing, the analyst encounters unexpected errors within the stored procedures. Which strategies should the analyst employ to effectively debug and troubleshoot these stored procedures within Snowflake?
Answer: A,B
Explanation:
Option A describes the correct method for obtaining the query ID of the failing procedure and querying the query history for detailed error information. Option B is also correct, implementing detailed error handling within the stored procedure provides greater insight into where the procedure fails. Option C is insufficient, Snowflake's general error messages are not always descriptive. Option D does not help during debugging and troubleshooting a running stored procedure. Option E is an inefficient and unreliable debugging approach.
NEW QUESTION # 49
Which of the following statements are true regarding the use of user-defined functions (UDFs) in Snowflake to optimize query performance, especially when compared to equivalent SQL expressions? (Select all that apply)
Answer: A,B,C,E
Explanation:
UDFs can improve code readability but don't always translate to performance gains. Java and Python UDFs incur overhead because they run outside the Snowflake engine. SQL UDFs are generally faster. They are useful for code reuse. External functions and UDFs are not the same: UDFs reside inside snowflake.
NEW QUESTION # 50
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?
Answer: D
Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.
NEW QUESTION # 51
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