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| Section | Weight | Objectives |
|---|---|---|
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
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NEW QUESTION # 38
In data presentations for business use analyses, what significance do identifying patterns and trends hold?
Answer: D
Explanation:
Identifying patterns and trends aids in insightful analyses in business use scenarios.
NEW QUESTION # 39
You are tasked with validating the data in a 'SALES table. The table contains 'ORDER ID', 'CUSTOMER ID', 'PRODUCT ID, 'SALE DATE, and 'SALE AMOUNT'. You need to identify orders where the 'SALE AMOUNT is negative or zero, indicating potential data entry errors, and also find orders with duplicate 'ORDER ID' values but different 'SALE AMOUNT values. Which of the following queries, when combined, would efficiently achieve this validation?
Answer: D
Explanation:
The first query 'SELECT ORDER ID, SALE_AMOUNT FROM SALES WHERE SALE AMOUNT O;' identifies orders with non- positive sale amounts. The second query 'SELECT ORDER_ID, FROM SALES GROUP BY ORDER_ID HAVING COUNT(DISTINCT SALE_AMOUNT) > 1;' efficiently finds ORDER_lDs that have multiple distinct SALE_AMOUNT values, indicating duplicates with conflicting data. "ANY _ VALUE is used since we only care about the 'ORDER_ID'.
NEW QUESTION # 40
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: A,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 # 41
You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?
Answer: E
Explanation:
Option C provides the most robust and efficient solution. Using 'LEFT OUTER JOIN' ensures that all transactions are included in the view, even if there is no matching economic data. 'TRY TO NUMBER handles malformed zip codes gracefully by converting valid zip codes to numbers and returning NULL for invalid ones, preventing errors. 'NVL' replaces NULL zip codes (either originally missing or resulting from TRY_TO_NUMBER) with a default value, allowing the join to proceed using a fallback. Adding the 'ENRICHMENT_SUCCESS' flag provides transparency about which records were enriched using the default zip code, enabling users to assess the reliability of the enriched data. Option A is inadequate because it ignores missing or malformed zip codes, leading to data loss. Option B is inefficient and not scalable due to row-by-row processing. Option D discards records with missing or malformed zip codes, resulting in significant data loss. Option E does not specifically handle data quality issues related to missing or malformed zip codes. Further the use of Tasks and materialized views, while increasing performance, doesn't necessarily address the issue of data quality.
NEW QUESTION # 42
You've identified a 'Filter' operation in a Snowflake query execution plan that is consuming a significant amount of time. The filter predicate involves a UDF (User-Defined Function) called 'calculate_score(columnl, column2)'. The UDF is written in Python. Analyzing the plan, you observe a high number of rows being processed by this filter. How can you optimize this scenario for faster query execution?
Answer: B,E
Explanation:
Options A and C provide significant performance improvements. A moves the computation to Snowflake's engine, likely improving speed. C pre-calculates the score, avoiding repeated UDF calls. While increasing warehouse size (B) might help, it doesn't address the fundamental inefficiency of the UDF. Caching within the UDF (E) can improve performance if there are repeated calls with the same inputs, but a SQL based code or materialized view is better solution in this case. Regular Expressions don't have computational power for any complex calculations.
NEW QUESTION # 43
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