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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q276-Q281):

NEW QUESTION # 276
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

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

Answer: A

Explanation:
Option C is the most effective for this scenario. Focusing on sessions and their durations provides a more meaningful feature for predicting conversion rates. Removing bot traffic (A) might be a useful preprocessing step but doesn't fundamentally address session-level relevance. Option B's logic is flawed removing all Internet Explorer traffic isn't inherently removing irrelevant data. Option D oversimplifies the data, losing valuable information about user behavior within sessions. Option E introduces bias by randomly sampling and removing potentially important patterns, plus it is too simplistic. The code example in C demonstrates how to calculate session duration using Snowpark functions, join the filtered session data back to the original data, and then drop the irrelevant columns.

NEW QUESTION # 277
You're deploying a pre-trained model for fraud detection that's hosted as a serverless function on Google Cloud Functions. This function requires two Snowflake tables: 'TRANSACTIONS (containing transaction details) and 'CUSTOMER PROFILES (containing customer information), to be joined and used as input for the model. The external function in Snowflake, 'DETECT FRAUD', should process batches of records efficiently. Which of the following approaches are most suitable for optimizing data transfer and processing between Snowflake and the Google Cloud Function?

A. Use Snowflake's Java UDF functionality to directly connect to the Google Cloud Function's database, bypassing the need for an external function or data transfer through HTTP.
B. Serialize the joined 'TRANSACTIONS' and 'CUSTOMER_PROFILES data into a large CSV file, store it in a cloud storage bucket, and then pass the URL of the CSV file to the 'DETECT FRAUD function.
C. Utilize Snowflake's external functions feature to send batches of data from the joined 'TRANSACTIONS' and 'CUSTOMER PROFILES tables to the 'DETECT_FRAUD function in a structured format (e.g., JSON) using HTTP requests. Implement proper error handling and retry mechanisms.
D. Within the 'DETECT FRAUD function, execute SQL queries directly against Snowflake using the Snowflake JDBC driver to fetch the necessary data from the "TRANSACTIONS' and 'CUSTOMER PROFILES' tables.
E. Create a Snowflake pipe that automatically streams new transaction data to the Google Cloud Function whenever new records are inserted into the 'TRANSACTIONS' table, triggering the fraud detection model in real-time.

Answer: C

Explanation:
Option D is the most appropriate. External functions are designed for this type of integration, allowing Snowflake to send batches of data to external services for processing. Using JSON provides a structured and efficient way to transfer the data. Option A is inefficient due to the overhead of writing and reading large files. Option B bypasses external functions which defeats the purpose of the question and also is not a standard integration pattern. Option C is not recommended as Snowflake is better at parallel processing. Option E would be appropriate for real- time streaming and fraud detection use case but involves much more setup than a single function invocation, so is a possible but not the most practical choice.

NEW QUESTION # 278
You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
B. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
C. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
D. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
E. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.

Answer: B

Explanation:
E is the LEAST appropriate. While clustering and training separate models per cluster can be a useful strategy for improving overall model performance by capturing heterogeneous patterns, it doesn't directly address the class imbalance problem within each cluster's dataset. Applying clustering does nothing about the class imbalance and adds unnecessary complexity. A, B, C, and D are all standard methods for handling class imbalance. A uses weighted training. B and D address resampling of the training set. C addresses the classification threshold.

NEW QUESTION # 279
You are tasked with building a data pipeline using Snowpark Python to process customer feedback data stored in a Snowflake table called FEEDBACK DATA'. This table contains free-text feedback, and you need to clean and prepare this data for sentiment analysis. Specifically, you need to remove stop words, perform stemming, and handle missing values. Which of the following code snippets and strategies, potentially used in conjunction, provide the most effective and performant solution for this task within the Snowpark environment?

A. Load the FEEDBACK DATA' table into a Pandas DataFrame using perform stop word removal and stemming using libraries like spacy or NLTK, handle missing values using Pandas' 'fillna()' method. Then, convert the cleaned Pandas DataFrame back into a Snowpark DataFrame. Use vectorization of text column in dataframe after above step
B. Implement all data cleaning tasks within a single SQL stored procedure including removing stop words using REPLACE functions, stemming using a custom lookup table, and handling NULL values using COALESC Call this stored procedure from Snowpark for Python.
C. Utilize Snowpark's 'call_function' with a Java UDF pre-loaded into Snowflake, which removes stop words and performs stemming with libraries like Lucene. Missing values can be handled with SQL's 'NVL' function during the initial data extraction into a Snowpark DataFrame.
D. Use a Python UDF that utilizes the NLTK library to remove stop words and perform stemming on the feedback text. Handle missing values by replacing them with an empty string using the .fillna(")' method on the Snowpark DataFrame after applying the UDF.
E. Leverage Snowflake's built-in string functions within SQL to remove common stop words based on a predefined list. Use a Snowpark DataFrame to execute this SQL transformation. For stemming, research and deploy a Java UDF implementing stemming algorithms, then chain it within a Snowpark transformation pipeline. Replace missing values with the string 'N/A' during the DataFrame construction using 'na.fill('N/A')'.

Answer: C,E

Explanation:
Options B and C provide the most effective and performant solutions.Option B leverages a combination of SQL and Java UDF to efficiently handle different parts of the cleaning process. The use of Snowflake's built-in string functions for removing stop words in SQL is efficient for common stop words, and Java UDF provides a more flexible and potentially more efficient solution for stemming. DataFrame .na.fill' is the most appropriate way to fill the missing values during the DataFrame creation. Option C: Utilizes pre-loaded Java UDFs for word processing, combined with SQL's NVL for missing value handling, is a strategy to leverage different components of Snowflake for performance and efficiency.Option A: While Python UDFs are flexible, they can be less performant than SQL or Java UDFs, especially for large datasets. Loading entire dataframe is an anti pattern. Also using .fillna on the dataframe instead of on the dataframe construction will reduce the performance. Option D: Loading all data into pandas is a bad habit and might reduce the performance. Also vectorization is not appropriate for cleaning the data. Option E: Stored procedures can be performant, relying solely on nested REPLACE functions for stop word removal can be cumbersome, and difficult to maintain compared to other approaches.

NEW QUESTION # 280
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. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
C. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
D. Create a materialized view containing pre-computed embeddings for all transaction descriptions.
E. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

Answer: B,C

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 # 281
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