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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Topic 2: Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Topic 3: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 4: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 5: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Topic 6: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 7: Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
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NEW QUESTION # 32
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?
Answer: D
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 33
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 # 34
You are tasked with loading data from a REST API that returns data in a deeply nested JSON format. The API requires authentication via an API key passed in the header. The JSON response contains an array of objects, each representing a customer order. You need to load only orders placed in the last 7 days. Which of the following steps and techniques would you use in Snowflake to achieve this?
Answer: B
Explanation:
Option B presents the most secure and robust approach. External Functions with API Integration objects allow calling external APIs. The filtering within the UDF ensures that only the required data is loaded, optimizing performance. Secure Secrets are essential for securely storing the API key. Option A is a viable solution but it requires careful security management of the API Key within Python code. Options C is incorrect since COPY INTO command doesn't support calling API with API Key in Header and it also require preprocessor function which are hard to implement. Option D is possible but doesn't mention API keys secure store which is less secure. Option E is not possible since 'SYSTEM$STREAM' object don't allow call API for data loading.
NEW QUESTION # 35
A Data Analyst created a model called modelX using SNOWFLAKE.ML.FORECAST. The Analyst needs to predict the next few values and save the result directly into tableX. What step does the Analyst need to take after calling the modelX!FORECAST function?
Answer: D
Explanation:
Snowflake Cortex ML functions, such as FORECAST, return a tabular result set when called using the instance method syntax (e.g., CALL modelX!FORECAST(...)). While this output is visible in the Snowsight results pane, the CALL statement itself cannot be used directly as a subquery within a standard INSERT INTO or CREATE TABLE AS SELECT (CTAS) statement.
To persist the results of a model's prediction into a permanent table (tableX), the Data Analyst must utilize the RESULT_SCAN table function. Snowflake stores the results of every query and function call in a temporary cache for 24 hours. The RESULT_SCAN function allows you to treat that cache as a queryable table.
The standard workflow is:
* Execute the forecast: CALL modelX!FORECAST(FORECASTING_PERIODS => 12);
* Immediately after, use the LAST_QUERY_ID() function to identify the query that generated the forecast results.
* Create the table by querying that result set: CREATE TABLE tableX AS SELECT * FROM TABLE (RESULT_SCAN(LAST_QUERY_ID())); Evaluating the Options:
* Option A is incorrect because the CALL syntax does not support a direct INTO clause for table creation.
* Option B is incorrect because passing a table as an argument is part of the training or input phase, not the output persistence phase.
* Option D is overly complex and contains non-standard terminology ("List the cache content").
* Option C is the 100% correct answer. It reflects the required "post-processing" step in the Snowflake Data Cloud to bridge the gap between procedural model calls and relational table storage.
NEW QUESTION # 36
You are a data analyst at a retail company. You want to enrich your sales data with weather information from the Snowflake Marketplace to analyze the impact of weather conditions on sales. You have a table 'SALES DATA' with columns 'TRANSACTION_DATE (DATE) and 'STORE (INTEGER). You subscribe to a weather data listing from the Snowflake Marketplace that provides weather information by date and location (latitude and longitude). The weather data is in a view called 'WEATHER_DATA' with columns 'DATE' (DATE), 'LATITUDE' (NUMBER), 'LONGITUDE' (NUMBER), and 'TEMPERATURE' (NUMBER). You need to write a SQL query to join these two datasets. However, the 'WEATHER DATA' does not have a 'STORE ID' and requires calculating distance from a known 'STORE LATITUDE' and 'STORE LONGITUDE' stored in a 'STORES' table. Which approach is the MOST efficient and accurate way to enrich 'SALES DATA with 'TEMPERATURE' from 'WEATHER DATA'?
Answer: E
Explanation:
Option C is the most efficient and accurate. Creating a table allows us to pre-calculate store locations. Then, using a 'CROSS JOIN' avoids nested loops, and filtering using the Haversine formula provides accurate proximity-based matching. 'QUALIFY' ensures you select only the closest weather station. Option A is inaccurate as it averages temperatures across all locations. Option B is inefficient due to row-by-row processing within a stored procedure. Option D, while potentially accurate, can suffer from performance issues associated with UDFs, especially when dealing with a large volume of data. Option E is incorrect as you can't update a View directly and the case statement will be difficult to maintain. The Haversine formula calculates the great-circle distance between two points on a sphere given their longitudes and latitudes.
NEW QUESTION # 37
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