FREQUENT DSA-C03 UPDATES - VALID TEST DSA-C03 TIPS

Frequent DSA-C03 Updates - Valid Test DSA-C03 Tips

Frequent DSA-C03 Updates - Valid Test DSA-C03 Tips

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If passing the DSA-C03 certification exam in a short time is a goal of yours, we're here to help you get there on your first attempt by providing you with DSA-C03 real exam dumps you need to succeed. We have three formats of DSA-C03 updated questions. This is done so that every Snowflake DSA-C03 exam applicant may find useful DSA-C03 study material here, regardless of how they want to learn.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

  • A. Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
  • B. Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
  • C. Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
  • D. Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.
  • E. Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.

Answer: B,C,E

Explanation:
Options B, C, and E are the correct strategies. Caching (B) reduces redundant calls. A queueing system (C) provides precise rate control but adds complexity. A retry mechanism with backoff (E) handles rate limit errors gracefully. Sleeping within a UDF (A) is inefficient and inaccurate, as it doesn't account for network latency or processing time. Scaling up the warehouse (D) might increase concurrency but won't directly address the per-second rate limit of the external API and could be cost-prohibitive.


NEW QUESTION # 90
You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

  • A. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
  • B. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
  • C. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
  • D. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
  • E. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.

Answer: A

Explanation:
Option E correctly identifies the most appropriate metrics (Precision, Recall, Fl-score, AUC-ROC, and Log Loss) for evaluating a binary classification model, especially in the context of imbalanced datasets. It also correctly describes the focus of each metric. Accuracy can be misleading with imbalanced datasets. MSE and R-squared are for regression problems (Option B). Confusion Matrix is a table, and Options D, contains incorrect statement.


NEW QUESTION # 91
A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

  • A. Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
  • B. Increase the learning rate significantly to speed up convergence during training.
  • C. Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
  • D. Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
  • E. Reduce the number of features by randomly removing some columns, as this always prevents overfitting.

Answer: D

Explanation:
Feature scaling is crucial for Logistic Regression. Features with different scales can disproportionately influence the model's coefficients. Snowflake UDFs allow for scalable data transformation within the platform. Increasing the learning rate excessively can lead to instability. Randomly removing features can remove important information. Deep neural networks require substantial tuning and resources and aren't always the best starting point and can have issues deploying inside of Snowflake. Ignoring missing values will negatively impact performance.


NEW QUESTION # 92
You are tasked with identifying fraudulent transactions in a large financial dataset stored in Snowflake using unsupervised learning. The dataset contains features like transaction amount, merchant ID, location, time, and user ID. You decide to use a combination of clustering and anomaly detection techniques. Which of the following steps and techniques would be MOST effective in achieving this goal while leveraging Snowflake's capabilities and minimizing false positives?

  • A. Use a Snowflake Python UDF to perform feature selection, apply a combination of K-means clustering and anomaly detection techniques like Isolation Forest or Local Outlier Factor (LOF), and then score each transaction based on its likelihood of being fraudulent. Tune parameters and use a hold-out validation set to minimize false positives, using a Snowpark DataFrame to retrieve the data.
  • B. Implement an Isolation Forest algorithm directly in SQL using complex JOINs and window functions to identify anomalies based on transaction volume and velocity.
  • C. Apply Principal Component Analysis (PCA) for dimensionality reduction, then use DBSCAN clustering to identify dense regions of normal transactions and flag any transaction that is not within a dense region as potentially fraudulent. After, review the anomalous data points.
  • D. Use only the 'transaction amount' feature and perform histogram-based anomaly detection in Snowflake SQL by identifying values outside of the common ranges, disregarding other potentially relevant information.
  • E. Perform K-means clustering on the entire dataset using all available features, then flag any transaction that falls outside of any cluster as fraudulent. Ignore any feature selection or engineering to simplify the process.

Answer: A,C

Explanation:
Option B leverages PCA for dimensionality reduction, improving clustering performance and reducing noise, followed by DBSCAN, which is effective at identifying outliers. Option D provides a comprehensive approach utilizing feature engineering, a combination of clustering and anomaly detection techniques implemented via Python UDF within Snowflake, and proper validation to minimize false positives. These approaches address data preprocessing, algorithm selection, and model evaluation for effective fraud detection. Option A lacks feature selection/engineering and may lead to poor clustering. Option C is inefficient and impractical. Option E is too simplistic and ignores crucial information.


NEW QUESTION # 93
You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

  • A. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
  • B. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
  • C. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
  • D. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.
  • E. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.

Answer: D,E

Explanation:
Options A and C are correct. Option A demonstrates calculating AUC directly within Snowflake using a Snowpark Python UDF and scikit-learn's . This is efficient for large datasets as it avoids data transfer. Option C correctly outlines the process of calculating TPR and FPR using SQL and approximating AUC using the trapezoidal rule, another viable approach within Snowflake. Option B is incorrect; AUC can be calculated reliably within Snowflake. Option D is inefficient due to data transfer. Option E is blatantly incorrect.


NEW QUESTION # 94
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