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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 2: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Topic 3: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Topic 4: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 5: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Topic 6: 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 7: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
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NEW QUESTION # 10
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?
Answer: B
Explanation:
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.
NEW QUESTION # 11
You are analyzing customer order data in Snowflake. A column 'ORDER DATE' is stored as VARCHAR. You notice inconsistencies: some dates are in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY', and some have missing values represented by 'N/A'. You need to standardize the 'ORDER DATE' column into a DATE format and handle missing values. What is the most efficient and robust Snowflake SQL statement to achieve this, ensuring no data is lost and that invalid dates are replaced with NULL?
Answer: E
Explanation:
Option B is the most robust and efficient because it uses REGEXP LIKE to validate the date formats before attempting to convert them using TRY_TO_DATE. It also directly creates a temporary table to perform the transformation, minimizing the risk of data loss during the process. Then the old table is replaced with the content of the temp table.
NEW QUESTION # 12
When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)
Answer: B,C
Explanation:
Assessing data collection frequency involves considering business requirements and the volume of data necessary for analysis.
NEW QUESTION # 13
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:
Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?
Answer: D
Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.
NEW QUESTION # 14
When selecting and implementing an effective data model, what considerations are crucial for ensuring its suitability for BI requirements? (Select all that apply)
Answer: C,D
Explanation:
Effective data models should ensure scalability, flexibility, good performance, and ease of maintenance to meet BI requirements effectively.
NEW QUESTION # 15
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