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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 2: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Topic 3: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Topic 4: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Topic 5: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Topic 6: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 7: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
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NEW QUESTION # 31
How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?
Answer: A
Explanation:
Time Travel feature allows querying historical data versions, facilitating historical data analysis and retrospective query optimizations based on past data states.
NEW QUESTION # 32
You've created a Snowflake dashboard for your company's sales team using a Bl tool. The dashboard displays real-time sales data pulled directly from Snowflake. The sales team wants to be automatically notified whenever a particular product's sales exceed a certain threshold in a given day. You need to implement a system to accomplish this. Which of the following methods offer a way to trigger notifications based on data changes in Snowflake AND integrate with an external notification system (e.g., Slack, Email)? Select all that apply:
Answer: C,E
Explanation:
A and B offer native Snowflake capabilities for triggering actions based on data conditions and integrating with external systems. Alerts are designed for real-time monitoring and can call external functions to send notifications. Tasks can be scheduled to perform periodic checks and trigger notifications through stored procedures and external APIs. C: Streams and Pipes are primarily for continuous data loading and transformation, not direct notification triggering based on data conditions. It's an indirect method. D: BI tools are not the primary component for handling real time alerting based on exceeding specific values. E: Replication is for disaster recovery/high availability and doesn't address real- time notifications.
NEW QUESTION # 33
Your company uses Snowflake to store sales data'. A dashboard reporting weekly sales trends is performing poorly. The underlying table, 'SALES DATA, contains billions of rows with columns like 'SALE DATE, 'PRODUCT ID', 'CUSTOMER D', and 'SALE AMOUNT. The dashboard queries use 'SALE DATE for filtering and grouping. The query execution plan shows full table scans. You need to optimize the dashboard's performance with minimal impact on data loading processes. Which of the following strategies should you implement FIRST to improve query performance?
Answer: A
Explanation:
Clustering the table on 'SALE DATE' is the most effective initial strategy. It physically organizes the data based on 'SALE DATE, which the dashboard queries use for filtering, thus reducing the amount of data scanned during query execution. Materialized views require ongoing maintenance and may not be the most efficient starting point. Increasing the warehouse size will increase the resource, but doesn't solve the underlying problem of full table scans, and search optimization is less efficient than clustering for date-based filtering. Snowflake does not support user-defined partitioning. Hence option A is the most appropriate choice.
NEW QUESTION # 34
Which actions are typically part of responding to data import errors in Snowflake? (Select all that apply)
Answer: A,B,D
Explanation:
Responding to data import errors involves identifying error sources, resolving inconsistencies, and analyzing logs for resolution.
NEW QUESTION # 35
When working with semi-structured data in Snowflake, how do built-in functions for traversing, flattening, and nesting aid in data manipulation?
Answer: A
Explanation:
Built-in functions for semi-structured data in Snowflake simplify handling complex and nested structures, making data manipulation more manageable and enhancing flexibility in data transformation.
NEW QUESTION # 36
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