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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
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. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: B,C

Explanation:
Option B is critical because discretization is essential for handling continuous variables in association rule mining. The way these variables are binned can significantly influence the rules discovered. Option C is also critical because in fraud detection, identifying rare but highly predictive rules is crucial. Low support rules, if they have high confidence and lift, can point to specific patterns indicative of fraud. Option A is incorrect because requiring high support would miss rare fraud patterns. Option D is incorrect because some high cardinality attributes might be important indicators.Option E is incorrect as Apriori algorith cannot be directly run using SQL, Snowpark and python is a good option.

NEW QUESTION # 163
You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

A. The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.
B. The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.
C. The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.
D. The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.
E. There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.

Answer: A

Explanation:
A 400 Bad Request error typically indicates that the server (Google Cloud A1 Platform in this case) received a request that it could not understand. This often means the data being sent is in an incorrect format or does not conform to the expected schema. While the other options could potentially cause issues, a 400 error is most directly linked to data type mismatches or schema violations. Diagnosing this involves carefully inspecting the data being sent by Snowflake and comparing it to the model's input requirements. Google Cloud logging or network tracing could be necessary in complex situations to identify discrepancies. The use of REQUEST and RESPONSE translators can mitigate these issues.

NEW QUESTION # 164
You are analyzing customer churn for a telecommunications company. You have a Snowflake table called 'CUSTOMER ACTIVITY with columns 'CUSTOMER ID', 'CALL DURATION_SUM' (total call duration in minutes), 'DATA USAGE GB' (total data usage in GB), 'CONTRACT LENGTH MONTHS', and 'CHURNED' (boolean indicating whether the customer churned). You want to understand the relationship between these features and churn. Specifically, you want to visualize the distribution of 'CALL DURATION SUM' for churned and non-churned customers. Which of the following visualizations, combined with appropriate Snowflake SQL to prepare the data, would BEST illustrate the relationship between 'CALL DURATION SUM' and 'CHURNED'?

A. A line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.
B. A histogram of 'CALL DURATION SUM" for churned customers and a separate histogram of "CALL DURATION SUM' for non-churned customers, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
C. A box plot with 'CHURNED on the x-axis and "CALL DURATION SUM' on the y-axis, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
D. A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'
E. A scatter plot with on the x-axis and 'CHURNED' (0 or 1) on the y-axis, generated directly from the table using an external visualization tool connected to Snowflake.

Answer: C

Explanation:
Option C is the best choice- A box plot effectively visualizes the distribution of ' CALL DURATION SUM' for each 'CHURNED' category (churned and non-churned). It shows the median, quartiles, and outliers, allowing for a clear comparison of the distribution of call durations between the two groups. The CTE allows for any required aggregation or filtering before sending the data to the visualization tool- A scatter plot (option A) is not ideal for visualizing distributions. Histograms (option B) can work, but box plots are often more concise and informative for comparing distributions across groups. A pie chart (option D) ignores 'CALL DURATION SUM'- Aline chart (option E) ignores individual customers and time, losing the ability to relate 'CALL DURATION SUM' and 'CHURNED' at the customer level.

NEW QUESTION # 165
A financial institution wants to use Snowflake Cortex to analyze customer reviews and feedback extracted from various online sources to gauge customer sentiment towards their new mobile banking application. The goal is to identify positive, negative, and neutral sentiments, and also extract key phrases that drive these sentiments. Which of the following steps represent a viable workflow for achieving this using Snowflake Cortex and related functionalities?

A. 1. Ingest the customer reviews into a Snowflake table. 2. Create a custom JavaScript UDF that calls the Snowflake Cortex 'COMPLETE' endpoint with a prompt that asks for both sentiment and key phrases. 3. Store the results in a new Snowflake table.
B. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKE.ML.PREDICT' function with a sentiment analysis model to determine the overall sentiment score for each review. 3. Apply a separate key phrase extraction model via 'SNOWFLAKE.ML.PREDICT' to identify important keywords in the reviews.
C. 1. Create a Streamlit application hosted externally that connects to the Snowflake database. 2. The Streamlit app uses a Python library like 'transformers' to perform sentiment analysis and key phrase extraction on the customer reviews read from Snowflake. 3. The results are then written back to a separate Snowflake table.
D. 1. Ingest the customer reviews into a Snowflake table. 2. Use Snowflake's built-in 'NLP_SENTIMENT' function (if available) or a similar UDF based on a pre- trained sentiment analysis model to get the sentiment score. 3. Use regular expressions in SQL to extract key phrases based on frequency and context.
E. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKML.PREDICT' function with the appropriate task-specific model to determine the sentiment score for each review. 3. Further fine-tune the sentiment model with customer review data to improve the score and accuracy.

Answer: B

Explanation:
Option A is the most viable workflow. It leverages Snowflake Cortex directly to perform both sentiment analysis and key phrase extraction. By using the 'SNOWFLAKE.ML.PREDICT' function with appropriate models, it keeps the processing within the Snowflake environment and avoids the need for external dependencies or custom coding (as in options B and C). The rest of the options are less effective because they involve use third party components when Snowflake Cortex readily has modules that can do what is required.

NEW QUESTION # 166
You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A. Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
B. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
C. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.
D. Create a materialized view containing pre-computed embeddings for all transaction descriptions.
E. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.

Answer: B,E

Explanation:
Option B is a better approach compared to option A to generate embeddings because its incrementally generate embeddings for new transactions. Option E is also an important approach where if transaction description remains same for the embeddings will not be re-computed. Materialized view is not suited for API integrations like those using Snowflake Cortex. Option D is technically correct, but doesn't address the optimization and cost concerns. Option A Regenerating embeddings for the entire dataset daily is computationally expensive and can quickly lead to high costs, especially with Snowflake Cortex. The best approach is to use caching and compute only for a new transaction description. So correct answer is B and E.

NEW QUESTION # 167
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