Instant Access Snowflake DSA-C03 Exam Premium Dumps - FreeCram

You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

• A. Option B
• B. Option E
• C. Option A
• D. Option C
• E. Option D

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

• A. Option B
• B. Option E
• C. Option A
• D. Option C
• E. Option D

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

• A. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.
• B. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
• C. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.
• D. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.
• E. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?

• A. Option B
• B. Option E
• C. Option A
• D. Option C
• E. Option D

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

• A. Option B
• B. Option E
• C. Option A
• D. Option C
• E. Option D

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).