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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 2: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Topic 3: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro |
| Topic 4: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 6: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 7: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
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NEW QUESTION # 42
When utilizing User-Defined Functions (UDFs) in SQL, which statement accurately describes their usage?
Answer: A
Explanation:
UDFs extend SQL functionalities by enabling custom operations on data, going beyond standard SQL capabilities.
NEW QUESTION # 43
How do statistics and built-in functions contribute to forecasting based on data?
Answer: A
Explanation:
Statistics and built-in functions facilitate the creation of custom forecasting models, providing flexibility.
NEW QUESTION # 44
How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?
Answer: B
Explanation:
Different chart types offer varied data representation, aiding better analysis in reports and dashboards.
NEW QUESTION # 45
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?
Answer: A,B,D,E
Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.
NEW QUESTION # 46
Which aggregate functions can be used to provide values for a given bucket in Snowsight charts? (Select THREE).
Answer: A,C,E
Explanation:
In Snowsight, when you create a chart, you define a bucket (the X-axis or a grouping dimension) and then select an aggregate function to determine the values displayed (the Y-axis). Snowsight provides a specific subset of standard SQL aggregate functions that are optimized for rapid, interactive visualization.
* Any Value (Option A): This function (equivalent to ANY_VALUE) is often used when a dimension is functionally dependent on the bucket, and you simply need to pluck a representative value from the group.
* Average (Option B): This is a fundamental aggregation for identifying the central tendency of a bucketed dataset.
* Max (Option F): Along with SUM, MIN, and COUNT, MAX is a standard, built-in option for visualizing extremes within a data bucket.
Evaluating the Options:
* Options C and D (Standard Deviation and Mode) are common statistical measures, but they are not available as direct, one-click options in the Snowsight chart aggregation menu; they would require custom SQL logic in the worksheet first.
* Option E (HLL) is a probabilistic estimation function. While powerful in SQL, it is not a standard visualization aggregation in the UI.
* Options A, B, and F are the 100% correct options. They represent the native, out-of-the-box functions available to a Data Analyst for summarizing data in Snowsight dashboards.
NEW QUESTION # 47
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