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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

A. Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
B. Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
C. Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
D. Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
E. Reduce the size of the input DataFrame by sampling the data.

Question 2

You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

A. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
B. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
C. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
D. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
E. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.

Question 3

You are tasked with deploying a pre-trained sentiment analysis model hosted externally using AWS SageMaker. The model endpoint requires an API key for authentication, and you want to score customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS. Which of the following steps are necessary to securely and efficiently integrate this external model with Snowflake, assuming you have already created a Snowflake stage to store secrets?

A. Create a secret object in Snowflake to store the API key. Grant appropriate privileges on the secret to the role that will execute the external function. Modify external function that references secure external stage.
B. Store the API key in an environment variable within the AWS Lambda function (if using API Gateway) that serves as an intermediary between Snowflake and SageMaker. Snowflake calls the API Gateway endpoint which relays the request to the SageMaker endpoint, and no specific configuration is needed on snowflake.
C. Create an external function in Snowflake that retrieves the API key from a secure Snowflake secret object. Grant USAGE privilege on the secret to the service account associated with the external function.
D. Create an external function in Snowflake that invokes the SageMaker endpoint, hardcoding the API key directly into the function definition for simplicity.
E. Use Snowflake's external functions to directly call the SageMaker endpoint from a SQL query, passing the customer review text as input. No separate secure external stage configuration is needed as long as Snowflake has internet access.

Question 4

You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A.

B.

C.

D.

E.

Question 5

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. 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.
B. 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.
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. 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.
E. 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.

Solutions: