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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 2: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
| Topic 3: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion |
| Topic 4: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Topic 5: 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 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Topic 7: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
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NEW QUESTION # 33
What types of Snowflake functions are available for data analysis and manipulation? (Select all that apply)
Answer: A,B,D
Explanation:
Snowflake functions include scalar, aggregate, and system functions for data analysis and manipulation.
NEW QUESTION # 34
You are working with a table named 'PRODUCT DETAILS' that contains a 'PRICE column stored as a VARCHAR. The data in this column has inconsistencies, including leading/trailing spaces, currency symbols (e.g., '$', 'O'), and different decimal separators ('.' And ','). Additionally, some values are represented as 'N/A' or an empty string. You need to clean and validate this data to ensure the "PRICE' column can be safely converted to a NUMERIC data type. Choose the set of SQL transformations that will correctly clean the PRICE column. (Select all that apply)
Answer: A,B,C,D
Explanation:
This question requires multiple correct answers. Options A, B, C, and D are all necessary for cleaning the 'PRICE' column. Option A handles the 'N/A' and empty string values by setting them to NULL. Option B removes leading/trailing spaces using the 'TRIM' function. Option C removes currency symbols using 'REGEXP REPLACES. Option D standardizes the decimal separator by replacing commas with periods using REPLACE. Option E is not correct because it attempts to change the data type of the 'PRICE column, but the column is not yet cleaned to allow for successful conversion to a NUMERIC data type.
NEW QUESTION # 35
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: A,D
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 # 36
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?
Answer: D
Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.
NEW QUESTION # 37
A single variant data column table RAW_SOURCE has the following JSON records:
A Data Analyst needs to get the value of the "f" field and have it in a consumable, tabular format. Which query should be used to meet this requirement?
Answer: B
Explanation:
Working with nested semi-structured data requires a deep understanding of Snowflake's FLATTEN function and path notation. In the provided JSON exhibit, the data is hierarchical: the root contains a key named
"events", which is an array containing multiple objects. Each object within that array contains the target field
"f".
To transform this into a "consumable, tabular format" where each instance of "f" appears as a separate row, the Data Analyst must "explode" the array. Simply using path notation like data:events:f (Option A) will fail or return NULL because the engine cannot directly map a single path through an array structure to multiple scalar results without an explicit relational transformation.
The LATERAL FLATTEN function is the standard tool for this task. It takes a semi-structured column (or a specific path within it) as an input and returns a relational result set. By specifying input => data:events, the Analyst instructs Snowflake to look specifically at the array. For every element found in that array, FLATTEN generates a new row in the result set. The output of the flatten operation includes a column named VALUE, which represents the specific element (in this case, an object) currently being processed.
Evaluating the Options:
* Option A and C are incorrect because they attempt to use direct pathing on an array. Without flattening, the query engine sees the array as a single block of data and cannot reach the "f" key inside individual array elements.
* Option B is incorrect because it flattens the root data object. While this works for top-level keys, it wouldn't automatically iterate through the "events" array to expose the "f" values inside it; it would treat the entire array as a single value.
* Option D is the 100% correct answer. It correctly targets the "events" array for flattening and then utilizes path notation (value:f) followed by the appropriate type casting (::number) to produce the final tabular output. This is a core competency for any SnowPro Advanced Data Analyst handling modern telemetry or event-based data.
NEW QUESTION # 38
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