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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Topic 2: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 3: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Topic 4: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 5: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Topic 7: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
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NEW QUESTION # 34
In Snowflake, how does the implementation of primary keys for tables contribute to data integrity?
Answer: A
Explanation:
Primary keys enforce uniqueness in table rows, ensuring data integrity by preventing duplicate entries within a table.
NEW QUESTION # 35
You are preparing a CSV file for ingestion into Snowflake, and you need to ensure that the data types are correctly interpreted. The CSV contains a column named 'transaction_amount' that sometimes contains values with leading zeros (e.g., '00123.45'). You want to load this data into a Snowflake table where 'transaction_amount' is defined as NUMBER(IO, 2). Without modifying the CSV file itself, how can you ensure that the leading zeros are handled correctly during the COPY INTO operation?
Answer: A
Explanation:
Option E is the correct approach. Snowflake implicitly handles leading zeros when casting a VARCHAR column to a NUMBER type using during the COPY INTO operation. This avoids modifying the original CSV file or requiring a separate transformation step after loading. Option A is incorrect, as implicit type conversion might not always work as expected. Option B and C are incorrect because 'STRIP NULL_VALUE' is not relevant in the case of leading zeros. Option D is viable, but less efficient than handling during load time.
NEW QUESTION # 36
What critical role do user-defined functions (UDFs) play in SQL for advanced data analysis?
Answer: B
Explanation:
UDFs extend SQL functionalities by enabling customized operations on data, going beyond standard SQL capabilities.
NEW QUESTION # 37
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?
Answer: E
Explanation:
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.
NEW QUESTION # 38
A retail company suspects a sudden drop in sales in the 'Electronics' category. You, as a data analyst, need to perform a diagnostic analysis. Which of the following data collection strategies would be MOST effective to pinpoint the root cause, considering the limitations of Snowflake's cost optimization?
Answer: B,D
Explanation:
Option A provides granular data for trend analysis, and option D brings qualitative data (support tickets) that can reveal underlying issues like product defects or shipping problems. Collecting ALL sales data (option B) is resource-intensive and inefficient for initial diagnostic analysis. Option C lacks the necessary granularity. Option E can be used to establish a baseline for comparison, but is not effecient due to sampling of data.
NEW QUESTION # 39
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