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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 2: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 3: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 4: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 5: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Topic 7: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
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NEW QUESTION # 66
What are important factors to consider when creating tables and views in Snowflake? (Choose threE.
Answer: A,B,D
NEW QUESTION # 67
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?
Answer: C
Explanation:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.
NEW QUESTION # 68
A data analyst needs to forecast sales for the next quarter based on historical sales data'. The data is stored in a table named 'SALES DATA' with columns 'SALE DATE (DATE) and 'SALES_AMOUNT (NUMBER). The analyst wants to use the 'HOLT WINTERS function for forecasting, considering both trend and seasonality. However, the data contains some missing values for a few 'SALE DATE entries. Which of the following approaches is the MOST appropriate to handle missing data points while ensuring accurate forecasting with 'HOLT WINTERS in Snowflake?
Answer: C
Explanation:
HOLT_WINTERS function requires continuous data for time series analysis, and missing values will lead to inaccurate forecasts. Option E is the most appropriate because Interpolation method such as linear interpolation will give you more precise result. Other approaches like direct use or Exclusion will result in wrong forecasting results.
NEW QUESTION # 69
A Data Analyst creates and populates the following table:
create or replace table aggr(v int) as select * from values (1), (2), (3), (4); The Analyst then executes this query:
select percentile_disc(0.60) within group (order by v desc) from aggr;
What will be the result?
Answer: C
Explanation:
The PERCENTILE_DISC (discrete percentile) function is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification and returns the value from the set that corresponds to that percentile. Unlike PERCENTILE_CONT, which interpolates between values to find a continuous result, PERCENTILE_DISC always returns an actual value from the input set.
In this scenario, we have a set of four values: $\{1, 2, 3, 4\}$. The query specifies a descending order (order by v desc), so the ordered set for the calculation is $\{4, 3, 2, 1\}$.
To find the discrete percentile, Snowflake calculates the cumulative distribution. For a set of $N$ elements, each element represents a percentile rank of $1/N$. With 4 elements, each covers 25% ($0.25$) of the distribution:
* Value 4: Cumulative Percentile $0.25$
* Value 3: Cumulative Percentile $0.50$
* Value 2: Cumulative Percentile $0.75$
* Value 1: Cumulative Percentile $1.00$
The PERCENTILE_DISC(0.60) function looks for the first value whose cumulative distribution is greater than or equal to the specified percentile ($0.60$).
* $0.25$ (Value 4) is not $\ge 0.60$.
* $0.50$ (Value 3) is not $\ge 0.60$.
* $0.75$ (Value 2) is the first value where the cumulative distribution is $\ge 0.60$.
Therefore, the result is 2. If the order had been ascending (ASC), the cumulative distribution would have been
$\{1: 0.25, 2: 0.50, 3: 0.75, 4: 1.00\}$, and the result for $0.60$ would have been 3. Understanding the impact of the ORDER BY clause within the WITHIN GROUP syntax is a critical skill for the Data Analysis domain of the SnowPro Advanced: Data Analyst exam.
NEW QUESTION # 70
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?
Answer: B
Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.
NEW QUESTION # 71
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