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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 2: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 3: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Topic 4: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 6: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Topic 7: Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
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NEW QUESTION # 39
A Data Analyst needs to generate a graphic that will identify any natural clusters in a data set containing details about completed orders. Which Snowsight chart should be used?
Answer: B
Explanation:
In the realm of Data Presentation and Data Visualization, selecting the appropriate chart type is critical for revealing the underlying structure of data. When an analyst is tasked with identifying "natural clusters," they are looking for groups of data points that share similar characteristics across two or more dimensions.
A Scatter Plot is the primary visualization tool for this purpose. By plotting two numeric variables (e.g., ORDER_AMOUNT on the X-axis and CUSTOMER_LOYALTY_SCORE on the Y-axis), the scatter plot allows the human eye to immediately detect density. If the data points form distinct "clouds" or groupings, these represent the clusters the analyst is searching for. In Snowsight, scatter plots are highly effective for exploratory data analysis, as they allow analysts to visualize the relationship between variables and spot outliers that may skew statistical summaries.
Evaluating the Options:
* Option A is incorrect because Line Charts are designed to show trends over time (sequential data).
They connect points in a specific order, which would obscure clustering logic by forcing a temporal connection between unrelated data points.
* Option B is incorrect because a Heat Grid (or Heatmap) is best used for comparing categories across two dimensions using color intensity. While it can show high-density areas, it aggregates data into
"bins" or cells, which can mask the fine-grained distribution required to see natural, point-based clusters.
* Option C is incorrect because Bar Charts are used for comparing discrete categories or showing distributions within a single dimension. They do not effectively show the correlation between two continuous variables needed for cluster identification.
* Option D is the 100% correct answer. The Scatter Plot is the standard analytical graphic for cluster detection and correlation analysis within the Snowflake Snowsight interface, providing the necessary granularity to see how individual orders or customers group together in a multi-dimensional space.
NEW QUESTION # 40
When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?
Answer: A
Explanation:
Parquet format in Snowflake optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution compared to other formats like CSV and JSON.
NEW QUESTION # 41
You have a table named 'product_sales' with columns 'product_id', 'sale date' , and 'sales_amount'. You need to calculate a 7-day moving average of the sales amount for each product. However, you also need to handle cases where there might be missing 'sale_date' entries for a product. How can you best achieve this using Snowflake window functions and without creating temporary tables or stored procedures?
Answer: A
Explanation:
Option B is the most appropriate. using 'RANGE BETWEEN INTERVAL '6 day' PRECEDING AND CURRENT ROW' will calculate the average of sales amounts for the previous 6 days, regardless of whether there are missing days in the 'sale_date' column. This provides a true 7-day moving average. 'ROWS BETWEEN' (Option A) would count rows, not days, potentially including data from more than 7 days if there are multiple sales on a single day, or less than 7 days if there are missing days. 'EXCLUDE CURRENT ROW' (Option C) would exclude the current row's sales amount from the average. 'RANGE BETWEEN 6 PRECEDING AND CURRENT ROW' (Option D) would not be valid Snowflake syntax for dates. Option E will calculate cumulative average, also that WHERE clause will lead to error.
NEW QUESTION # 42
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?
Answer: D,E
Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.
NEW QUESTION # 43
When selecting data for building dashboards, which factors should be considered to ensure relevance and usability? (Select all that apply)
Answer: B,C
Explanation:
To ensure relevant and usable dashboards, data should be evaluated based on business requirements and filtered for irrelevant attributes.
NEW QUESTION # 44
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