PASS GUARANTEED USEFUL SNOWFLAKE - DSA-C03 - RELIABLE SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM DUMPS EBOOK

Pass Guaranteed Useful Snowflake - DSA-C03 - Reliable SnowPro Advanced: Data Scientist Certification Exam Dumps Ebook

Pass Guaranteed Useful Snowflake - DSA-C03 - Reliable SnowPro Advanced: Data Scientist Certification Exam Dumps Ebook

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q238-Q243):

NEW QUESTION # 238
A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?

  • A. Ignore transaction attributes that have a large number of distinct values (e.g., specific location coordinates) as they will likely lead to an explosion of rules and make interpretation difficult.
  • B. Focus solely on rules with very high support (e.g., > 0.1) to ensure statistical significance and avoid overfitting to rare fraudulent events.
  • C. Prioritize rules with high confidence and lift, even if support is relatively low, as rare but highly predictive combinations of attributes can be strong indicators of fraudulent activity.
  • D. Carefully discretize continuous variables like 'transaction amount' and 'time of day' into meaningful categories to enable association rule mining, and consider the impact of different discretization strategies on the resulting rules.
  • E. Ensure that the Apriori algorithm is run directly within Snowflake using SQL to maximize performance and scalability, rather than extracting the data and processing it in an external Python environment.

Answer: C,D

Explanation:
Option B is critical because discretization is essential for handling continuous variables in association rule mining. The way these variables are binned can significantly influence the rules discovered. Option C is also critical because in fraud detection, identifying rare but highly predictive rules is crucial. Low support rules, if they have high confidence and lift, can point to specific patterns indicative of fraud. Option A is incorrect because requiring high support would miss rare fraud patterns. Option D is incorrect because some high cardinality attributes might be important indicators.Option E is incorrect as Apriori algorith cannot be directly run using SQL, Snowpark and python is a good option.


NEW QUESTION # 239
A marketing team is using Snowflake to store customer data including demographics, purchase history, and website activity. They want to perform customer segmentation using hierarchical clustering. Considering performance and scalability with very large datasets, which of the following strategies is the MOST suitable approach?

  • A. Utilize a SQL-based affinity propagation method directly within Snowflake. This removes the need for feature scaling and specialized hardware.
  • B. Randomly sample a small subset of the customer data and perform hierarchical clustering on this subset using an external tool like R or Python with scikit-learn. Assume that results generalize well to the entire dataset. Avoid using Snowflake for this purpose.
  • C. Directly apply an agglomerative hierarchical clustering algorithm with complete linkage to the entire dataset within Snowflake, using SQL. This is computationally feasible due to SQL's efficiency.
  • D. Employ BIRCH clustering with Snowflake Python UDF. Configure Snowflake resources accordingly. Optimize the clustering process. And tune parameters.
  • E. Perform mini-batch K-means clustering using Snowflake's compute resources through a Snowpark DataFrame. Take a large sample of each mini-batch and perform hierarchical clustering on each mini-batch and then create clusters of clusters.

Answer: D

Explanation:
Hierarchical clustering has a high time complexity, making it impractical for large datasets. While mini-batch K-means provides the most efficient option for large datasets. BIRCH is more suited for huge datasets and can be applied as a Snowflake Python UDF with Snowpark DataFrames to provide scalability and high performance as its better than other clustering such as affinity propagation. Options A and E are impractical due to the computational cost of hierarchical clustering in SQL or affinity propagation in SQL. Sampling (Option C) can lead to inaccurate results.


NEW QUESTION # 240
You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?

  • A. Store the private key in a secure database table within Snowflake and query it dynamically.
  • B. Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
  • C. Store the private key in a secure vault (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and retrieve it dynamically within your VS Code script using the appropriate API or SDK.
  • D. Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.
  • E. Store the encrypted private key in a configuration file within your VS Code project and decrypt it at runtime using a password-based encryption algorithm.

Answer: C

Explanation:
Storing the private key in a secure vault like HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault is the most secure approach. These vaults are designed to securely store and manage sensitive information like private keys. They offer features like access control, auditing, and encryption at rest and in transit. Dynamically retrieving the key minimizes the risk of accidental exposure compared to storing it in configuration files or environment variables, even when encrypted. Options A, C, D, and E pose significant security risks.


NEW QUESTION # 241
You're building a model to predict whether a user will click on an ad (binary classification: click or no-click) using Snowflake. The data is structured and includes features like user demographics, ad characteristics, and past user interactions. You've trained a logistic regression model using SNOWFLAKE.ML and are now evaluating its performance. You notice that while the overall accuracy is high (around 95%), the model performs poorly at predicting clicks (low recall for the 'click' class). Which of the following steps could you take to diagnose the issue and improve the model's ability to predict clicks, and how would you implement them using Snowflake SQL? SELECT ALL THAT APPLY.

  • A. Generate a confusion matrix using SQL to visualize the model's performance across both classes. Example SQL:
  • B. Implement feature engineering by creating interaction terms or polynomial features from existing features using SQL, to capture potentially non-linear relationships between features and the target variable. Example:
  • C. Increase the complexity of the model by switching to a non-linear algorithm like Random Forest or Gradient Boosting without performing hyperparameter tuning, as more complex models always perform better.
  • D. Reduce the amount of training data to avoid overfitting. Overfitting is known to produce low recall for the 'click' class.
  • E. Calculate precision, recall, F I-score, and AUC for the 'click' class using SQL queries to get a more detailed understanding of the model's performance on the minority class. Example:

Answer: A,B,E

Explanation:
A, B, and C are correct. A is necessary to understand how many false negatives and false positives exist for each label. B is the direct measures to quantify recall, precision, Fl-score and AUC. C is also a standard technique, because the original data did not capture possible non-linear relationship between features and target variables. D and E are incorrect. Simply changing to a non-linear algorthim without proper tuning does not guarantee better result. Reducing training data is unlikely to have a positive effect, as overfitting tends to occur when we have too many features compared to training data.


NEW QUESTION # 242
You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

Answer: D

Explanation:
Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.


NEW QUESTION # 243
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