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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q145-Q150):

NEW QUESTION # 145
You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

A. Implement a data drift detection mechanism. Monitor the distribution of input features over time and trigger retraining if significant drift is detected using tools such as Snowflake's Anomaly Detection features or custom drift metrics calculated in SQL.
B. Use cross-validation techniques (e.g., k-fold cross-validation) during the retraining process to estimate the model's performance on unseen data and prevent overfitting. Evaluate on a held-out validation set.
C. Monitor the model's performance on a live dataset and trigger retraining only when the performance drops below a predefined threshold, using metrics like accuracy, precision, or recall. Save Model Performance to 'MODEL_PERFORMANCE.
D. Update the UDF in place using 'CREATE OR REPLACE FUNCTION' immediately after retraining completes, regardless of the validation results.
E. Retrain the model using the entire available dataset, as this will maximize the amount of data the model learns from.

Answer: A,B,C

Explanation:
B, C, and D are the most critical steps. Option B is essential because data drift can significantly impact model performance. Detecting and addressing data drift is crucial for maintaining accuracy over time. Option C is vital for preventing overfitting and ensuring the model generalizes well to unseen data. Cross-validation provides a more robust estimate of model performance than a single train-test split. Option D is necessary to ensure that the retraining process is only triggered when the model's performance degrades. Monitoring live data and using performance metrics as triggers is a key component of automated retraining. Option A is incorrect because retraining on the entire dataset without validation can lead to overfitting. Option E is dangerous, as it deploys the retrained model without confirming its effectiveness.

NEW QUESTION # 146
You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

Answer: D

Explanation:
Option C is the most efficient and correct solution. Snowflake's 'DIRECTORY table function provides metadata about files in a stage, including 'relative_path', 'size', and By querying this table and ordering by in descending order with 'LIMIT 1 , you can directly retrieve the information for the latest file without resorting to external tools like 'awk' or 'grep'. The '-csv' flag ensures that the output is easily parsable. Options A and D rely on parsing the output of the command, which is less reliable and efficient. Option B downloads file and uses system 'IS command. Option E gives information about stage, not files.

NEW QUESTION # 147
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 E
B. Option D
C. Option A
D. Option C
E. Option B

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 # 148
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

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

Answer: D

Explanation:
Option C is the best prompt because it directly instructs the LLM to act as a product expert and provide only the feature description, minimizing extraneous conversation or information. Options A and B lack specific instructions, potentially leading to verbose responses. Option D includes all product details in the prompt, which might overwhelm the LLM. Option E tries to fetch a specific feature description, but the SQL is incorrect. Correct SQL will increase token usage and may not lead to a concise response.

NEW QUESTION # 149
You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

Answer: D

Explanation:
Option C is correct. It uses 'SYSTEM$GET FILE(@IMAGE STAGE/image.jpg', to retrieve the image file as a binary object and then to encode it as a base64 string before passing it to the 'CLASSIFY_IMAGE external function. Option A is incorrect as it attempts to directly use a file format which is not relevant for sending the image content. Option B is incorrect because the image needs to be base64 encoded first. Option D is incorrect as it converts binary to JSON, which is not the required format. Option E is incorrect because BLOB TO BASE64' doesn't exists in Snowflake. TO BASE64 is correct method.

NEW QUESTION # 150
......

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