最新SnowPro Advanced DSA-C03考試題庫，Snowflake DSA-C03課程的擬真試題下載

問題1
You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

A. Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.

B. Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.

C. Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.

D. Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.

E. Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.

正確答案: B,C,D

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問題2
You are developing a churn prediction model and want to track its performance across different model versions using the Snowflake Model Registry. After registering a new model version, you need to log evaluation metrics (e.g., AUC, F 1-score) and custom tags associated with the training run. Assuming you have a registered model named 'churn_model' with version 'v2', which of the following code snippets demonstrates the correct way to log these metrics and tags using the Snowflake Python Connector and the 'ModelRegistry' API?

A.

B.

C.

D.

E.

正確答案: E

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問題3
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?

A. Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.

B. Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.

C. Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).

D. Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.

E. Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.

正確答案: D,E

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問題4
You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

A. Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.

B. Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.

C. Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using

D. Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.

E. Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.

正確答案: B,C,D

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問題5
You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A. Precision, Recall, and Fl-score.

B. ROC AUC and Recall.

C. Accuracy and Recall.

D. Accuracy and ROC AUC.

E. Precision and Fl-score.

正確答案: A

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問題6
You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?

A. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.

B. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.

C. Perform a chi-squared test. Null Hypothesis: There is no relationship between transaction amount and whether a transaction is fraudulent. Alternative Hypothesis: There is a relationship between transaction amount and whether a transaction is fraudulent.

D. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.

E. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.

正確答案: B

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問題7
You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

A. Option B

B. Option E

C. Option A

D. Option D

E. Option C

正確答案: D

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問題8
You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A. Select a threshold of 0.5, as this is a common default threshold for binary classification problems.

B. Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.

C. Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.

D. Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.

E. Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.

正確答案: E

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問題9
You are analyzing sensor data collected from industrial machines, which includes temperature readings. You need to identify machines with unusually high temperature variance compared to their peers. You have a table named 'sensor _ readings' with columns 'machine_id', 'timestamp', and 'temperature'. Which of the following SQL queries will help you identify machines with a temperature variance that is significantly higher than the average temperature variance across all machines? Assume 'significantly higher' means more than two standard deviations above the mean variance.

A. Option B

B. Option E

C. Option A

D. Option D

E. Option C

正確答案: C

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問題10
A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

A. First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.

B. Creating a temporary table in Snowflake using a SQL query that aggregates items by transaction and represents them in a format suitable for Snowpark's 'mlxtend' library. Then load this temporary table into a Snowpark DataFrame and use it as input to the Apriori algorithm.

C. Using Snowpark's 'DataFrame.groupBy(V and functions to aggregate items by transaction ID, then pivoting the data using to create the transaction-item matrix. This approach requires loading all data into the Snowpark DataFrame before pivoting.

D. Employing a custom UDF (User-Defined Function) written in Java or Scala that directly processes the transactional data within Snowflake and outputs the transaction-item matrix in a format suitable for Snowpark. This offloads processing to compiled code within Snowflake, maximizing performance.

E. Utilizing Snowflake's SQL function within a stored procedure to concatenate items purchased in each transaction into a string, then processing the string using Python in Snowpark to create the transaction-item matrix. This approach minimizes data transfer but introduces string parsing overhead in Python.

正確答案: C

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問題11
You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.

B. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.

C. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.

D. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

E. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.

正確答案: A

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問題12
You are tasked with preparing a Snowflake table named 'PRODUCT REVIEWS' for sentiment analysis. This table contains columns like 'REVIEW ID, 'PRODUCT ID', 'REVIEW TEXT', 'RATING', and 'TIMESTAMP'. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the 'PRODUCT REVIEWS' table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.

A. creating a new table 'REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , 'REVIEW_ID' , and 'RATING') using 'CREATE TABLE AS SELECT. SQL: 'CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'

B. Creating a VIEW that only selects the 'REVIEW _ TEXT , 'REVIEW_ID', and 'RATING' columns, effectively hiding the irrelevant columns from the model. SQL: 'CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'

C. Using 'ALTER TABLE DROP COLUMN' to directly remove 'TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: 'ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'

D. Dropping rows with 'NULL' values in REVIEW_TEXT and then dropping the 'PRODUCT_ID' and 'TIMESTAMP' columns using 'ALTER TABLE. SQL: 'CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'

E. All of the above.

正確答案: E

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問題13
You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

A. The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.

B. The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.

C. There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.

D. The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.

E. The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.

正確答案: E

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