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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q27-Q32):
NEW QUESTION # 27
You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?
Answer: C
Explanation:
Option D is the most robust and cost-effective solution. Using a Dynamic Table ensures that retraining is triggered only when necessary (when accuracy drops below the threshold). The Dynamic Table's materialization event then kicks off a Snowpark ML model training stored procedure that automatically retrains the model. This stored procedure saves the new model with a timestamp and updates a metadata table, allowing for version control. This eliminates unnecessary retraining runs (cost savings) and provides full lineage of models. Option A can be wasteful as it retrains even if it's not required. Option B using Stream & Pipes doesn't trigger model re-training after data accuracy breach. Option C doesn't account for model performance leading to unnecessary retrains. Option E introduces external dependencies and complexity that are best avoided within the Snowflake ecosystem.
NEW QUESTION # 28
You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?
Answer: A,C
Explanation:
Options A and E are the most effective because they provide a quantitative and statistically sound way to measure data drift. Calculating the KS statistic (Option A) for each feature allows you to identify which features have drifted the most. Calculating Jensen-Shannon Divergence on the predicted probability distributions will tell how much the prediction patterns have changed in the newer data, which helps in assesing drift. Option B is manual and subjective. Option C might lead to model instability without understanding the nature of the drift. Option D, while helpful for initial exploration, might not be sensitive enough to detect subtle but important drifts. Option E provides insight specifically into the model's output behavior shifts.
NEW QUESTION # 29
You are tasked with analyzing the 'transaction amounts' column in the 'sales data' table to understand its variability across different geographical regions. You need to calculate the variance of transaction amounts for each region. However, some regions have very few transactions, which can skew the variance calculation. Which of the following SQL statements correctly calculates the variance for each region, excluding regions with fewer than 10 transactions, using Snowflake's native statistical functions?
Answer: D
Explanation:
The correct answer is D. VAR_SAMP calculates the sample variance, which is appropriate for estimating the population variance from a sample. The HAVING clause correctly filters out regions with fewer than 10 transactions after the grouping is done. Option A is incorrect because it calculates the population variance. Option B and C are incorrect because the WHERE clause is applied before grouping, so cannot be directly used to filter groups based on size. Option E calculates the population variance, but this is also acceptable, depending on the scenario, where we need population variance rather than sample variance.
NEW QUESTION # 30
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?
Answer: C,D
Explanation:
Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .
NEW QUESTION # 31
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'?
Answer: A
Explanation:
Option E is the most correct. It includes the correct Snowflake UDF syntax, specifies the required packages (snowflake-snowpark- python, scikit-learn, pandas), imports the model from the stage, and defines a handler class with a 'predict' method that loads the model using pickle and performs the prediction. It also correctly utilizes the to access files from the stage. Other options have errors in syntax, file access within the UDF environment or how input features are handled.
NEW QUESTION # 32
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