Study with DSA-C03 most valid questions & verified answers

You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

• A. Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
• B. Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
• C. Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
• D. Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
• E. Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.

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

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A data scientist needs to analyze website session data stored in a Snowflake table named 'WEB SESSIONS'. The table contains columns like 'SESSION D', 'USER_ID, 'PAGE_VIEWS', 'TIME SPENT_SECONDS', and 'TIMESTAMP. They want to identify potential bot traffic by analyzing the correlation between 'PAGE VIEWS' and 'TIME SPENT SECONDS'. Which of the following Snowflake SQL queries is the MOST efficient and statistically sound way to calculate the Pearson correlation coefficient between these two columns, handling potential NULL values appropriately?

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

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You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?

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

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A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

• A. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
• B. The CLT is applicable, and the sample mean will converge to the population median.
• C. The CLT is not applicable because the population distribution (call durations) is heavily skewed.
• D. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
• E. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.

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