優秀的DSA-C03最新試題和資格考試中的領先材料供應商＆最熱門的DSA-C03下載選擇PDFExamDumps可以100%幫助你通過考試。我們根據Snowflake DSA-C03的考試科目的不斷變化，也會不斷的更新我們的培訓資料，會提供最新的考試內容。PDFExamDumps可以為你免費提供24小時線上客戶服務，如果你沒有通過Snowflake DSA-C03的認證考試，我們會全額退款給您。 最新的 SnowPro Advanced DSA-C03 免費考試真題 (Q263-Q268):問題 #263
You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?
A. Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
B. Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
C. Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.
D. Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
E. Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
答案：A
解題說明：
Option D is the best first step. Data quality monitoring and PSI allow for quantifying and identifying data drift. SHAP (B) is useful after determining that concept drift is the problem. Retraining immediately (A) without understanding the cause can exacerbate the problem. Reverting (C) is a temporary fix, not a solution. Adjusting the threshold (E) without understanding the underlying issue is also not a proper diagnostic approach.
問題 #264
You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?
A. Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
B. Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.
C. Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
D. Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
E. Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
答案：A,B,D
解題說明：
Options B, C and E are correct. Caching (B) reduces calls to the external prediction service, minimizing both latency and cost, especially for redundant transactions. Batching (C) amortizes the overhead of invoking the external function and reduces the number of API calls to Google Cloud, improving throughput. Asynchronous invocation (E) allows Snowflake to continue processing without waiting, improving responsiveness. Option A is incorrect, as it will be a very slow and costly process. Option D mentions training the model which is unrelated to the prediction goal and would involve different steps involving the external function and model training.
問題 #265
You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?
A. Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
B. Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.
C. Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.
D. Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.
E. Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
答案：B,C
解題說明：
Options A and C are the most effective and scalable. A leverages Snowflake's SQL capabilities and window functions for in-database processing, making it efficient for large datasets. C utilizes Snowflake's native anomaly detection capabilities (if available and configured), providing a built-in solution. Option B is not scalable due to data export limitations. Option D might be valid but can be less performant than SQL window functions. Option E uses sampling, which might not accurately represent the entire dataset's outliers and could lead to inaccurate fraud detection.
問題 #266
You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?
A. Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
B. Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
C. Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
D. Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
E. Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
答案：A,E
解題說明：
Option B is the most comprehensive. It establishes a system for continuous monitoring of model performance using real-world feedback, and alerts you when performance degrades. Option E is also strong because it allows for direct comparison of a new model against the existing model in a production setting, identifying model decay before it significantly impacts performance. Options A and D are insufficient for monitoring as they lack real-world feedback loops for continuous assessment. Simply retraininig frequently does not guarantee model improvements, and option C relies on manual intervention and lacks granular monitoring of the model's specific performance. Shadow Deployment is costly but more robust.
問題 #267
You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.
A. Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
B. Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
C. Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
D. Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
E. Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
答案：B,C,D
解題說明：
Options B, C and D are correct. B: A large model requires sufficient memory, so using an XLARGE or larger warehouse is crucial. C: Snowpark Container Services are designed for such scenarios and is the recommended best practice. D: Specifying the model file as an import and using lazy loading helps manage memory efficiently. Option A can work, but since 'Ilm_model.pt' is already compressed. Compressing again will be not efficient. Splitting the model into chunks (Option E) is overly complicated. Option C gives flexibility of calling out functions from containerized environment, so better scalability.