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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: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 3: Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| 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: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Topic 7: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
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질문 # 36
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?
정답：A
질문 # 37
You are responsible for loading data into a Snowflake table named 'CUSTOMER DATA' from a series of compressed JSON files located in a Google Cloud Storage (GCS) bucket. The data volume is significant, and the loading process needs to be as efficient as possible. The JSON files are compressed using GZIP, and they contain a field called 'registration date' that should be loaded as a DATE type in Snowflake. However, some files contain records where the 'registration_date' is missing or has an invalid format. Your goal is to load all valid data while skipping any files that contain invalid dates, and log any files that contain invalid records. You want to choose the most efficient approach. Which of the following options represents the best strategy to achieve this?
정답：E
설명：
The correct answer is E. Using gracefully handles invalid dates by returning NULL, which can be managed using a default value on the target table. 'ON ERROR = CONTINUE' ensures the loading process doesn't halt. Combining this with a default value provides for a fast, efficient load. Option A skips the entire file, which is not desired if only some records are invalid. Option B will halt the load process if the target field cannot accept a value. Option C is valid but requires a separate process. Option D makes the manual handling more complex since Snowpipe is designed for near real time instead of batch. E is the best option as all invalid fields will be populated with the default value and load will be unaffected.
질문 # 38
Which aspects are crucial for making predictions based on data for forecasting purposes? (Select all that apply)
정답：A,B
설명：
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.
질문 # 39
Consider the following chart.
What can be said about the correlation for sales over time between the two categories?
정답：D
설명：
In Data Analysis, correlation refers to a statistical relationship between two variables. When analyzing a time- series chart like the one provided, a Data Analyst looks for patterns in how the two categories-"Enterprise" (blue line) and "Pro Edition" (yellow line)-move in relation to one another over the X-axis (Year).
A Positive Correlation would be indicated if both lines generally moved in the same direction at the same time (e.g., when Enterprise sales increase, Pro Edition sales also increase). A Negative Correlation (or inverse correlation) would be shown if the lines moved in opposite directions consistently (e.g., when one peaks, the other troughs).
Looking closely at the provided exhibit, the fluctuations for both editions are highly erratic and appear independent of each other. For instance, around the year 2008, the Pro Edition (yellow) shows a significant peak while the Enterprise edition (blue) experiences a sharp decline. Conversely, in other sections of the chart, they both dip or rise simultaneously by chance, but there is no sustained, predictable pattern of movement. The peaks and valleys do not align in a way that suggests one variable's movement is tied to the other.
Statistically, this lack of a discernible relationship indicates a Correlation Coefficient near zero. In the context of the Snowflake Snowpro Advanced: Data Analyst exam, identifying "No Correlation" is a key skill for interpreting Snowsight visualizations. It tells the analyst that the factors driving sales for the Enterprise tier are likely distinct from those driving the Pro Edition, and they should be analyzed as independent segments rather than interdependent variables. Therefore, based on the visual evidence of random, non- synchronous movement across the timeline, the only supported conclusion is that there is no correlation.
질문 # 40
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?
정답：D
설명：
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.
질문 # 41
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