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DAA-C01 exam dumps: questions 101 to 200 of 267

Page 2 of the free DAA-C01 question bank for the SnowPro® Advanced: Data Analyst exam. Questions 101 to 200 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

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Questions 101 to 105 of 267

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DAA-C01 Question 101

Single answerAI functions

A retail analytics team stores customer product reviews in a Snowflake table and wants analysts to classify each review into one of five business-defined categories (Shipping, Product Quality, Pricing, Customer Service, Other) directly in SQL. The team needs a solution that minimizes custom model management and works natively inside Snowflake for ad hoc analysis. Which approach should the analyst use?

A
Use SNOWFLAKE.CORTEX.CLASSIFY_TEXT with the review text and the five category labels to return the best matching label.
B
Use SNOWFLAKE.CORTEX.COMPLETE and assume the model output will always match one of the five categories exactly without additional prompt design or validation.
C
Use AI_EMBED and assign the review to a category by selecting the largest numeric value in the embedding vector.
D
Use AI_SUMMARIZE_AGG across all reviews first, then classify the single aggregated summary into one of the five categories for row-level reporting.

Show answer and explanation

Correct answer: A

Explanation

For native AI-driven text classification in Snowflake SQL, the best fit is SNOWFLAKE.CORTEX.CLASSIFY_TEXT, which is intended for assigning text to predefined labels. This aligns with the scenario's requirement for minimal model management and direct use inside Snowflake. While SNOWFLAKE.CORTEX.COMPLETE can sometimes be used for prompt-based classification, it is a generative function and usually requires stricter prompt engineering and output validation to make results reliable for production analytics. AI_EMBED is for vector representations used in semantic similarity workflows, and AI_SUMMARIZE_AGG is for summarization across rows rather than row-level labeling. These distinctions follow Snowflake Cortex AI function best practices: use task-specific functions when available, and reserve general generation functions for cases that need free-form output or custom prompting.

A. Correct.
Correct. SNOWFLAKE.CORTEX.CLASSIFY_TEXT is designed for text classification tasks in SQL where you provide the input text and a set of candidate labels. This is the most direct native approach for mapping each review to one of a fixed set of business categories without building and hosting a custom model pipeline.
B. Incorrect.
Incorrect. SNOWFLAKE.CORTEX.COMPLETE can generate text and could be prompted to classify reviews, but treating its output as guaranteed to match one of the five labels exactly is risky. Generative output may vary in wording, formatting, or confidence unless carefully constrained and post-validated. For a fixed-label classification use case, CLASSIFY_TEXT is the more appropriate built-in function.
C. Incorrect.
Incorrect. AI_EMBED generates vector embeddings that represent semantic meaning, but the embedding values themselves are not category IDs and cannot be classified by simply choosing the largest number in the vector. Embeddings are typically used for similarity search, clustering, or retrieval workflows, not direct label assignment in this way.
D. Incorrect.
Incorrect. AI_SUMMARIZE_AGG is an aggregation function that summarizes multiple rows into a combined summary. Using it before classification would lose row-level detail and would not support assigning a category to each individual review, which is required for analyst reporting.

DAA-C01 Question 102

Single answerUser-Defined Functions (UDFs)

A data analyst team stores raw event payloads in a VARIANT column named EVENT_DATA. Many dashboards need the same transformation to extract a nested product category, convert it to uppercase, and return NULL when the path does not exist. To avoid repeating the logic in dozens of queries, the team wants to implement a reusable function in Snowflake. Which approach best meets this requirement while staying aligned with Snowflake UDF capabilities and SQL-based analytics workflows?

A
Create a SQL scalar UDF that accepts a VARIANT argument and returns VARCHAR using logic such as UPPER(event_data:"product":"category"::STRING).
B
Create a table UDF because only table UDFs can read nested fields from VARIANT and return NULL when the path is missing.
C
Create an aggregate UDF so the category extraction can be reused across SELECT statements without repeating the expression.
D
Create a Python UDF because SQL UDFs cannot reference keys inside VARIANT values or perform string functions like UPPER on extracted values.

Show answer and explanation

Correct answer: A

Explanation

The best answer is to use a SQL scalar UDF because the requirement is to encapsulate a repeated row-level transformation into a reusable function for analytic queries. In Snowflake, scalar UDFs are intended for exactly this use case: accepting input values and returning one value per row. Snowflake SQL can directly query VARIANT data with path notation, cast extracted values to native SQL types, and apply built-in functions such as UPPER. Missing paths in semi-structured data typically evaluate to NULL, which aligns with the stated requirement. Table UDFs are used when a function must return multiple rows or columns, while aggregate UDFs are for combining values across rows. Python UDFs are valid in Snowflake, but best practice is to prefer SQL UDFs for simple SQL-native transformations because they are simpler to maintain and better aligned with standard analytics workflows. Relevant Snowflake documentation includes SQL UDFs, table functions, and querying semi-structured data in VARIANT columns.

A. Correct.
Correct. A SQL scalar UDF is the most appropriate choice when encapsulating reusable expression logic that returns a single value per row. Snowflake SQL can directly navigate semi-structured data in VARIANT using path notation, cast the extracted value to STRING, and apply string functions such as UPPER. If the path is missing, Snowflake returns NULL for the extraction, which satisfies the requirement without extra complexity.
B. Incorrect.
Incorrect. A table UDF returns a result set, not a single scalar value, so it is not the best fit for a reusable per-row expression that outputs one VARCHAR value. Snowflake does not require a table UDF to read nested VARIANT fields; SQL expressions and scalar UDFs can do that directly.
C. Incorrect.
Incorrect. Aggregate UDFs are designed to compute a result over multiple input rows, similar to SUM or AVG. The scenario requires transforming each row's VARIANT payload into a single category value, so an aggregate UDF would be the wrong abstraction.
D. Incorrect.
Incorrect. Python UDFs can be useful for specialized logic, but they are not required here. Snowflake SQL UDFs fully support semi-structured data access and built-in SQL functions such as UPPER. Choosing Python would add unnecessary complexity and runtime overhead for a straightforward SQL transformation.

DAA-C01 Question 103

Single answerUser-Defined Functions (UDFs)

A data analyst team stores semi-structured clickstream events in a VARIANT column named EVENT_DATA. Analysts repeatedly need to extract a normalized marketing channel from different JSON paths and apply the same logic across dashboards and ad hoc SQL queries. The team wants to centralize this transformation in Snowflake so it can be called directly from SQL, return a single text value, and remain easy to maintain. Which approach best meets this requirement?

A
Create a SQL scalar UDF that accepts a VARIANT input, uses Snowflake SQL expressions to inspect the relevant JSON paths, and returns a VARCHAR channel name.
B
Create a user-defined table function (UDTF) that returns one row per possible channel, then join it back to each event record when only a single channel value is needed.
C
Create an aggregate UDF so the channel can be derived during GROUP BY operations, even when analysts need the value at the individual event level.
D
Create a stored procedure that returns a result set containing the channel and require analysts to call it instead of using the logic inline in SELECT statements.

Show answer and explanation

Correct answer: A

Explanation

The best answer is to create a SQL scalar UDF. In Snowflake, scalar UDFs are intended to accept input parameters and return a single value, making them ideal for reusable row-level transformations such as normalizing fields extracted from VARIANT data. This allows analysts to call the function directly in SQL statements used by BI tools, worksheets, and views. A UDTF would be appropriate only if the logic needed to emit multiple rows or columns per input. Aggregate functions are for calculations across sets of rows, not deriving a single attribute from one record. Stored procedures are better suited for workflow orchestration and procedural tasks than inline query expressions. Snowflake documentation and best practices distinguish these object types by return shape and execution pattern: scalar UDFs for single-value expressions, UDTFs for tabular output, and stored procedures for procedural logic.

A. Correct.
Correct. A SQL scalar UDF is the most appropriate choice when the requirement is to encapsulate reusable logic that takes an input value such as VARIANT and returns a single value such as VARCHAR for use directly in SQL expressions. This matches the scenario exactly: analysts want a centralized transformation callable from SELECT statements, filters, and other SQL logic. Snowflake SQL UDFs are well suited for wrapping JSON path extraction, CASE logic, COALESCE, and casting into a reusable function.
B. Incorrect.
Incorrect. A UDTF is designed to return a set of rows rather than a single scalar value. While it could be made to work, it is unnecessarily complex and less natural for this use case because each event needs one derived channel value usable inline in standard SQL expressions. Choosing a table function here reflects the misconception that any reusable logic should be implemented as a table-returning object.
C. Incorrect.
Incorrect. Aggregate functions operate across multiple rows and are intended for summarized calculations, not row-level derivations from a single event payload. The team needs to derive a channel for each event record before or independent of aggregation. This option confuses row-wise transformation requirements with grouped analytic calculations.
D. Incorrect.
Incorrect. Stored procedures are useful for procedural orchestration, control flow, and administrative or multi-step operations, but they are not the best mechanism for a reusable expression that analysts need directly inside ad hoc queries and dashboards. Analysts typically want to call a function in a SELECT list or WHERE clause, which is what a scalar UDF is designed for. This option reflects the misconception that procedures and UDFs are interchangeable for query-time value derivation.

DAA-C01 Question 104

Single answerML functions

A retail analytics team stores daily product sales in Snowflake and wants to create a next-7-day demand forecast for each product-store combination directly in SQL. They need a solution that minimizes data movement, supports forecasting from time-series data already in Snowflake, and can be operationalized by analysts without building a custom external ML pipeline. Which approach best fits this requirement?

A
Use the built-in forecasting ML function in Snowflake to generate forecasts from the historical time-series data grouped by product and store.
B
Export the sales data to an external notebook environment, train a custom ARIMA model there, and write the predictions back to Snowflake because Snowflake ML functions cannot perform forecasting.
C
Use a classification ML function in Snowflake to predict the next 7 days of numeric demand because classification models are designed for future-value estimation.
D
Create a materialized view over the sales table and rely on automatic refresh to produce future demand estimates without using any ML function.

Show answer and explanation

Correct answer: A

Explanation

The best answer is to use Snowflake's native forecasting ML capability for time-series prediction directly on data stored in Snowflake. This is the most practical choice when the goal is to keep data in-platform, reduce operational complexity, and enable SQL-centric analysts to build forecasts without managing external training infrastructure. In Snowflake documentation and product guidance, built-in ML functions are intended for common predictive tasks such as forecasting and anomaly detection on data already in Snowflake. By contrast, exporting data to external tools adds unnecessary movement and orchestration overhead, classification is the wrong model category for numeric future demand prediction, and materialized views do not provide machine learning or forecasting behavior.

A. Correct.
Correct. Snowflake provides built-in ML functions for common predictive tasks, including forecasting, that can be applied directly to data stored in Snowflake. This approach aligns with the requirement to minimize data movement and enable analysts to work in SQL. For a retail time-series use case with historical sales by product-store combination, the forecasting function is the most appropriate native option.
B. Incorrect.
Incorrect. Exporting data to an external environment is possible in some architectures, but it does not best fit the stated requirement. The scenario specifically prioritizes minimizing data movement and avoiding a custom external ML pipeline. Also, the premise is wrong because Snowflake does provide native ML functionality for forecasting use cases.
C. Incorrect.
Incorrect. Classification functions are intended to predict categorical outcomes, such as churn/no churn or fraud/not fraud. Forecasting numeric demand over future dates is a time-series regression/forecasting problem, not a classification problem. Choosing classification here reflects a misunderstanding of ML task types.
D. Incorrect.
Incorrect. Materialized views improve query performance for repeated computations over existing data, but they do not generate predictive outputs for future periods. Automatic refresh keeps the view synchronized with underlying data; it does not perform forecasting or infer unseen future values.

DAA-C01 Question 105

Single answerML functions

A retail analytics team stores historical daily sales in Snowflake and wants to generate short-term demand forecasts directly in SQL for thousands of product-store combinations. They need a solution that minimizes data movement, supports forecasting across many independent time series, and returns confidence intervals so planners can evaluate uncertainty. Which approach should the data analyst recommend?

A
Use the SNOWFLAKE.ML.FORECAST function on the historical data, grouping by product and store, and generate predictions with prediction intervals directly in Snowflake.
B
Use the SNOWFLAKE.ML.CLASSIFY function because demand forecasting is a categorical prediction problem when predicting whether sales go up or down.
C
Export the data to an external notebook, train a separate ARIMA model for each product-store combination, and reload the results because Snowflake ML functions do not support multiple time series.
D
Use the SNOWFLAKE.ML.ANOMALY_DETECTION function because anomaly scores can be converted into future sales estimates with confidence intervals.

Show answer and explanation

Correct answer: A

Explanation

The best answer is to use SNOWFLAKE.ML.FORECAST directly in Snowflake. For a large retail forecasting use case with many product-store combinations, Snowflake ML Functions are designed to reduce operational complexity by keeping data in-platform and allowing SQL-based model creation and inference. FORECAST is the appropriate ML function for time-series prediction, and it supports multiple series through identifier columns so each product-store combination can be modeled as a separate series. It also returns forecast results with interval bounds, which helps business users assess uncertainty in the predictions. By contrast, CLASSIFY is for categorical labels, not numeric demand forecasting, and ANOMALY_DETECTION identifies outliers rather than future values. Snowflake documentation for ML Functions and FORECAST describes in-database forecasting workflows, including handling multiple time series and returning prediction interval information, which directly matches this scenario.

A. Correct.
Correct. Snowflake ML Functions include forecasting capabilities designed to work directly on data in Snowflake. FORECAST is appropriate for time-series prediction tasks such as daily sales forecasting. It supports forecasting for multiple series by specifying series identifiers, which fits the product-store scenario. It also provides forecast output with interval information, which is important when planners need uncertainty estimates. This aligns with the requirement to minimize data movement and perform forecasting in SQL within Snowflake.
B. Incorrect.
Incorrect. CLASSIFY is intended for classification use cases where the target is a discrete label, such as churn/not churn or fraud/not fraud. Forecasting daily sales is a regression/time-series problem, not a classification problem. Converting the business question into 'up or down' would lose the required numeric sales forecast and would not satisfy the need for confidence intervals on future sales values.
C. Incorrect.
Incorrect. While exporting data and training external models is possible, it does not meet the stated goal of minimizing data movement. More importantly, the claim that Snowflake ML functions do not support multiple time series is wrong. FORECAST is specifically designed to operate in Snowflake and can handle many independent series when configured appropriately. This option reflects a common misconception that time-series forecasting in Snowflake must be done outside the platform.
D. Incorrect.
Incorrect. ANOMALY_DETECTION is used to identify unusual observations relative to historical patterns, not to produce forward-looking forecasts. Anomaly scores are not equivalent to predicted future values, and they do not serve as forecast confidence intervals. This option confuses monitoring for unusual behavior with estimating future demand.

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DAA-C01 practice questions 101 to 200 of 267

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