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[General] DSA-C03 Prüfungsfragen, DSA-C03 Fragen und Antworten, SnowPro Advanced: Data Sci

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【General】 DSA-C03 Prüfungsfragen, DSA-C03 Fragen und Antworten, SnowPro Advanced: Data Sci

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Snowflake SnowPro Advanced: Data Scientist Certification Exam DSA-C03 Prüfungsfragen mit Lösungen (Q57-Q62):57. Frage
A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.
  • A.
  • B.
  • C.
  • D.
  • E.
Antwort: D
Begründung:
Option E is the MOST robust and efficient. It uses the interquartile range (IQR) method, which is less sensitive to extreme outliers than the z-score method in Option A. It also utilizes 'approx_quantile' and is therefore more optimized for Snowflake large datasets. The median is also a more robust measure of central tendency for imputation than the mean when dealing with outliers. Option C uses a hard-coded threshold for outlier removal and imputes with 0, which is not adaptive or robust. Option D skips data cleaning altogether.Option A uses z-score which may work however, since IoT has continuous streaming data quantile based outlier removal is better. It is more optimised for large dataset and better at handling streaming datasets.

58. Frage
You've deployed a fraud detection model in Snowflake. The model is implemented as a Python UDF that uses a pre-trained scikit-learn model stored as a stage file. Your goal is to enable near real-time fraud detection on incoming transactions. Due to regulatory requirements, you need to maintain a detailed audit trail of all predictions, including the input features, model version, prediction scores, and any errors encountered during the prediction process. Which of the following approaches are valid and efficient for storing these audit logs and predictions in Snowflake?
  • A. Utilize Snowflake's Streams and Tasks to automatically capture changes to the transaction table and trigger the prediction UDF, storing the audit logs in a separate table with similar structure as described in option A.
  • B. Log the audit information to an external logging service (e.g., Splunk) using an external function called from within the UDF.
  • C. Store the audit logs as unstructured text files in an external stage (e.g., AWS S3) and periodically load them into a Snowflake table using COPY INTO command.
  • D. Use Snowflake's 'SYSTEM$QUERY LOG' table to extract information about the UDF execution and join it with the transaction data to reconstruct the audit trail.
  • E. Create a dedicated table with columns for transaction ID, input features (as a JSON VARIANT), model version, prediction score, error message (if any), and prediction timestamp. Use a Snowflake Sequence to generate unique log IDs.
Antwort: A,E
Begründung:
Options A and C are the most valid and efficient approaches. Option A provides a structured and readily queryable format for the audit logs, making it easy to analyze and report on fraud detection performance. Using a SEQUENCE ensures unique and ordered log IDs. Option C leverages Snowflake's Streams and Tasks to automate the prediction process and audit logging, ensuring that all transactions are processed and logged in near real-time. This is particularly suitable for continuous fraud detection. Option B is less efficient due to the overhead of loading unstructured data and parsing it. It lacks real-time processing capabilities. Option D introduces external dependencies and potential latency. While external logging services can be valuable, storing the audit data natively in Snowflake provides better integration and performance. Option E is not reliable for recreating the full audit trail, as primarily captures query execution metadata and may not contain all the necessary information (e.g., input features, model version). Also SYSTEM$QUERY LOG data availability can be delayed.

59. Frage
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. 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.
  • 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. 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.
  • D. 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.
  • E. 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.
Antwort: D
Begründung:
A 400 Bad Request error typically indicates that the server (Google Cloud A1 Platform in this case) received a request that it could not understand. This often means the data being sent is in an incorrect format or does not conform to the expected schema. While the other options could potentially cause issues, a 400 error is most directly linked to data type mismatches or schema violations. Diagnosing this involves carefully inspecting the data being sent by Snowflake and comparing it to the model's input requirements. Google Cloud logging or network tracing could be necessary in complex situations to identify discrepancies. The use of REQUEST and RESPONSE translators can mitigate these issues.

60. Frage
You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

  • A. Option D
  • B. Option A
  • C. Option E
  • D. Option B
  • E. Option C
Antwort: C
Begründung:
Option E correctly calculates the expected payout by subtracting the cost of false positives (investigating non-fraudulent transactions) from the loss due to false negatives (undetected fraudulent transactions). The confusion matrix data (50 false negatives, 20 false positives) translates to an expected payout of (1000 50) - (50 20) = $49000 loss for every 1000 transactions. The other queries either incorrectly combine the costs and losses, or only calculate one aspect. The other query calculate in correct format or not relevant as per context.

61. Frage
You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)
  • A. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.
  • B. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
  • C. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
  • D. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.
  • E. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.
Antwort: A
Begründung:
The correct answer is C. Precision is calculated as True Positives / (True Positives + False Positives) = 80 / (80 + 10) = 0.89. Recall is calculated as True Positives / (True Positives + False Negatives) = 80 / (80 + 20) = 0.80. Accuracy is calculated as (True Positives + True Negatives) / Total = (80 + 90) / 200 = 0.85. High precision indicates fewer false positives, while lower recall indicates more false negatives. Also the select statement calculates true positives, true negatives, false positives, and false negatives from churn_predictions table and then accuracy, precision , recall has to be calculated.

62. Frage
......
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