SNOWFLAKE DSA-C03 ANSWERS REAL QUESTIONS | EXAM DSA-C03 SIMULATOR FEE

Snowflake DSA-C03 Answers Real Questions | Exam DSA-C03 Simulator Fee

Snowflake DSA-C03 Answers Real Questions | Exam DSA-C03 Simulator Fee

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

  • A. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.
  • B. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
  • C. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
  • D. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.
  • E. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used

Answer: D

Explanation:
Option E is the most suitable because it explicitly addresses the temporal dependency and prevents data leakage by creating sequential, non-overlapping folds based on 'TRANSACTION DATE. Options A and D rely on potentially incorrect assumptions by Snowflake about time series data and are unlikely to provide the correct cross-validation folds. Option B can introduce leakage because it treats dates as categorical variables and performs random assignment. Option C performs the cross validation entirely outside of Snowflake, which negates the benefits of Snowflake's scalability and data proximity.


NEW QUESTION # 80
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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.

Answer: B,E

Explanation:
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.


NEW QUESTION # 81
A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

Answer: A,D

Explanation:
Options B and C correctly calculates the standard deviation. Option B utilizes 'NVL' , which is the equivalent of 'COALESCE or ' IFNULL', to handle NULL Customer Segment values, and 'STDDEV_SAMP' for sample standard deviation, which is generally the correct function to use when dealing with a sample of the entire population. Option C also uses 'COALESCE and utilizes the 'STDDEV POP function, which returns the population standard deviation, assuming the data represents the whole population. Option A uses IFNULL, which works, and STDDEV, which is an alias for either STDDEV SAMP or STDDEV POP. The exact behavior will depend on session variable setting. Option D also uses 'CASE WHEN' construct which works to identify Unknown segments. STDDEV is again aliased. Option E calculates the variance and not Standard deviation.


NEW QUESTION # 82
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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.

Answer: B

Explanation:
Option D is the most appropriate. External functions are designed for this type of integration, allowing Snowflake to send batches of data to external services for processing. Using JSON provides a structured and efficient way to transfer the data. Option A is inefficient due to the overhead of writing and reading large files. Option B bypasses external functions which defeats the purpose of the question and also is not a standard integration pattern. Option C is not recommended as Snowflake is better at parallel processing. Option E would be appropriate for real- time streaming and fraud detection use case but involves much more setup than a single function invocation, so is a possible but not the most practical choice.


NEW QUESTION # 83
You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

  • A. RobustScaler (using interquartile range)
  • B. MinMaxScaler (Min-Max scaling)
  • C. No scaling is needed as tree-based models are robust to skewed data.
  • D. Power Transformer (Yeo-Johnson or Box-Cox)
  • E. StandardScaler (Z-score normalization)

Answer: A,D

Explanation:
RobustScaler is suitable for handling outliers as it uses the interquartile range, which is less sensitive to extreme values than the mean and standard deviation used by StandardScaler. PowerTransformer can also be useful for transforming skewed data to a more Gaussian-like distribution, which can improve the performance of some machine learning models. While tree-based models are generally more robust to skewed data than other models, scaling can still improve convergence speed or performance, especially when combined with other preprocessing techniques or models that are sensitive to feature scaling. Therefore, E is not a great choice. Using RobustScaler and PowerTransformer will lead to a better performance of model.


NEW QUESTION # 84
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