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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 3: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 4: 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 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: Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Topic 7: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
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NEW QUESTION # 43
You need to load data from a set of CSV files located in an AWS S3 bucket into a Snowflake table called 'SALES DATA. The CSV files have a header row, and you want to automatically detect the data types of the columns. However, some files might have inconsistent data types (e.g., a column that is usually an integer might occasionally contain a string). Furthermore the file are compressed using GZIP. Which of the following COPY INTO command options can you use to achieve this goal while handling potential data type mismatches?
Answer: C
Explanation:
The 'ON_ERROR = 'SKIP_RECORD" option allows the COPY INTO command to skip individual records that have data type mismatches or other errors during the load process. This ensures that the load process continues even if some records fail validation. 'SKIP_FILE' would skip entire file, 'CONTINUE' will only continue the process with the given data, and 'SKIP_FILE' skips the file. The VALIDATE UTF8 isn't directly relevant to data type issues
NEW QUESTION # 44
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)
Answer: D,E
Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.
NEW QUESTION # 45
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)
Answer: B,D,E
Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.
NEW QUESTION # 46
Which action is essential in performing exploratory ad-hoc analyses?
Answer: A
Explanation:
Ad-hoc analysis involves using queries to explore patterns and anomalies beyond predefined routines.
NEW QUESTION # 47
A company stores web analytics data in a Snowflake table named 'WEB EVENTS. This table includes a 'USER ID column, a 'TIMESTAMP' column indicating when the event occurred, and a 'EVENT TYPE column that captures the type of event (e.g., 'page_view', 'add_to_cart', 'purchase'). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?
Answer: E
Explanation:
Window functions are the most efficient approach for calculating aggregate values (like minimum and maximum) within partitions (in this case, per user) without requiring self-joins or subqueries. Correlated subqueries can be inefficient for large datasets. Stored procedures with iteration are generally slower than set-based operations. Lateral views are more suitable for exploding array structures, not for finding min/max values. A simple GROUP BY would provide the overall minimum and maximum, not per user.
NEW QUESTION # 48
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