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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q157-Q162):

NEW QUESTION # 157
A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?

A. Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
B. Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
C. Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.
D. Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.
E. Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.

Answer: B

Explanation:
Snowpark and Python UDFs provide a way to execute code within the Snowflake environment, leveraging its compute resources and keeping data within Snowflake's security and governance boundaries. This avoids data egress and is more efficient than exporting data or attempting to implement K-means directly in SQL. While B is potentially viable, D leveraging DataFrames provides further optimization. The other options are either inefficient or insecure.

NEW QUESTION # 158
You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

A. Power Transformer (Yeo-Johnson or Box-Cox)
B. StandardScaler (Z-score normalization)
C. MinMaxScaler (Min-Max scaling)
D. No scaling is needed as tree-based models are robust to skewed data.
E. RobustScaler (using interquartile range)

Answer: A,E

Explanation:
RobustScaler is suitable for handling outliers as it uses the interquartile range, which is less sensitive to extreme values than the mean and standard deviation used by StandardScaler. PowerTransformer can also be useful for transforming skewed data to a more Gaussian-like distribution, which can improve the performance of some machine learning models. While tree-based models are generally more robust to skewed data than other models, scaling can still improve convergence speed or performance, especially when combined with other preprocessing techniques or models that are sensitive to feature scaling. Therefore, E is not a great choice. Using RobustScaler and PowerTransformer will lead to a better performance of model.

NEW QUESTION # 159
You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

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

Answer: E

Explanation:
Option E is the most efficient because it leverages Snowpark's built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using 'regexp_replace' and then uses 'iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using 'regexp_replace' , requires an additional 'with_column' to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined 'call_udf function and 'snowflake-snowpark-python' library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.

NEW QUESTION # 160
A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A. The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
B. The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
C. Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
D. The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
E. The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.

Answer: A,B,E

Explanation:
Option A is correct because support represents the proportion of transactions that contain both the antecedent and the consequent. Option D is correct because confidence represents the proportion of transactions containing the antecedent that also contain the consequent. Option E is correct because lift = confidence / (probability of consequent). Therefore, lift of 1.2 means confidence is 1.2 times the probability of the consequent. Hence 20% more likely than the baseline. Option B is incorrect because lift, not confidence, captures the relative likelihood compared to the baseline. Option C is incorrect because a lift > 1 suggests a positive correlation, not a negative one.

NEW QUESTION # 161
You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

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

Answer: A,B,C

Explanation:
Options A, B, and C are all valid ways to perform one-hot encoding in Snowflake. Option A uses the standard 'CASE statement, Option B leverages the 'IFF function (inline IF), and Option C uses 'DECODE , all achieving the same result of creating binary indicators for each category. Option D is incorrect because it uses GET DDL, which retrieves DDL statements, not for comparison. Option E is incorrect because it does not represent three seperate columns of binary columns for each customer type. Therefore, options A, B, and C are the correct approaches to generate separate binary columns for one-hot encoding.

NEW QUESTION # 162
......

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Exam DSA-C03 Simulator Free | Reliable DSA-C03 Braindumps Pdf

100% Pass Quiz 2026 Snowflake DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Newest Exam Simulator Free

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q157-Q162):

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