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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q194-Q199):

NEW QUESTION # 194
You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A. The input feature data types in the table do not match the expected data types by the scikit-learn model. Cast the input columns to the correct data types (e.g., FLOAT, INT) before passing them to the UDTF. Use explicit casting functions like 'TO DOUBLE and INTEGER in your SQL query.
B. The issue is related to the immutability of the Snowflake execution environment for UDTFs. To resolve this, cache the loaded model instance within the UDTF's constructor and reuse it for subsequent predictions. Using a global variable is also acceptable.
C. There may be an error in model, where the 'predict method is producing different ouputs for the same inputs. Retraining the model will resolve the issue.
D. The scikit-learn model was not properly serialized and deserialized within the UDTF. Ensure the model is saved using 'joblib' or 'pickle' with appropriate settings for cross-platform compatibility and loaded correctly within the UDTF's 'process' method. Verify serialization/deserialization by testing it independently from Snowflake first.
E. The UDTF is not partitioning data correctly. Ensure the UDTF utilizes the 'PARTITION BY clause in your SQL query based on a relevant dimension (e.g., 'customer_id') to prevent state inconsistencies across partitions. This will isolate the impact of any statefulness within the function

Answer: A,D

Explanation:
Options A and C address the most common causes of inconsistent UDTF predictions with scikit-learn models. A covers the essential aspect of correct serialization/deserialization for model persistence and retrieval in the Snowflake environment, which ensures model state consistency. C focuses on the critical data type compatibility between the input data and the model expectations, which, if mismatched, can lead to unexpected prediction variations. Option B is incorrect, the model should be loaded in the process method. Option D is only relevant if you are using a stateful model, but it is still not the most likely cause. Option E is incorrect as the Model prediction method gives deterministic ouput for given inputs.

NEW QUESTION # 195
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?

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

Answer: B

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 # 196
You're building a model to predict whether a user will click on an ad (binary classification: click or no-click) using Snowflake. The data is structured and includes features like user demographics, ad characteristics, and past user interactions. You've trained a logistic regression model using SNOWFLAKE.ML and are now evaluating its performance. You notice that while the overall accuracy is high (around 95%), the model performs poorly at predicting clicks (low recall for the 'click' class). Which of the following steps could you take to diagnose the issue and improve the model's ability to predict clicks, and how would you implement them using Snowflake SQL? SELECT ALL THAT APPLY.

A. Reduce the amount of training data to avoid overfitting. Overfitting is known to produce low recall for the 'click' class.
B. Implement feature engineering by creating interaction terms or polynomial features from existing features using SQL, to capture potentially non-linear relationships between features and the target variable. Example:
C. Generate a confusion matrix using SQL to visualize the model's performance across both classes. Example SQL:
D. Increase the complexity of the model by switching to a non-linear algorithm like Random Forest or Gradient Boosting without performing hyperparameter tuning, as more complex models always perform better.
E. Calculate precision, recall, F I-score, and AUC for the 'click' class using SQL queries to get a more detailed understanding of the model's performance on the minority class. Example:

Answer: B,C,E

Explanation:
A, B, and C are correct. A is necessary to understand how many false negatives and false positives exist for each label. B is the direct measures to quantify recall, precision, Fl-score and AUC. C is also a standard technique, because the original data did not capture possible non-linear relationship between features and target variables. D and E are incorrect. Simply changing to a non-linear algorthim without proper tuning does not guarantee better result. Reducing training data is unlikely to have a positive effect, as overfitting tends to occur when we have too many features compared to training data.

NEW QUESTION # 197
You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?

A. Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.
B. Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.
C. Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
D. Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
E. Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.

Answer: A,E

Explanation:
Options A and C are the most effective and scalable. A leverages Snowflake's SQL capabilities and window functions for in-database processing, making it efficient for large datasets. C utilizes Snowflake's native anomaly detection capabilities (if available and configured), providing a built-in solution. Option B is not scalable due to data export limitations. Option D might be valid but can be less performant than SQL window functions. Option E uses sampling, which might not accurately represent the entire dataset's outliers and could lead to inaccurate fraud detection.

NEW QUESTION # 198
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?

A. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
B. Explicitly specify a in the call.
C. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
D. Create an associated Stream on the source tables used for Feature Groups
E. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.

Answer: A,C

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 # 199
......

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