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Free PDF Snowflake - DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam–High Pass-Rate Dumps Discount

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

Answer: C,E

Explanation:
Options B and E are correct. They both calculate the 12 week rolling average grouped by week correctly and will display the average. Option B is the more correct of the two, because it does not require the user to sort the result to get the appropriate rolling average. Option A is incorrect because rangeBetween with seconds is not appropriate for weekly aggregation and calculation. Option C is incorrect because to_date would truncate the time component, grouping everything with the same date. Option D calculates a cumulative average since the beginning of the dataset

NEW QUESTION # 108
You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

A. Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
B. Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
C. Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.
D. Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
E. Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.

Answer: C

Explanation:
Option D is the most secure. Key pair authentication is more secure than username/password. Specifying a dedicated virtual warehouse ensures dedicated compute. Option A is highly insecure. Option B doesn't directly create a Snowpark session. Option C, while using OAuth, requires proper setup and key pair provides more control. Option E is highly insecure and grants excessive privileges.

NEW QUESTION # 109
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
B. Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
C. Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.
D. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
E. Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.

Answer: B,E

Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.

NEW QUESTION # 110
A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A. The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
B. The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
C. Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
D. The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
E. The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.

Answer: A,D,E

Explanation:
Option A is correct because support represents the proportion of transactions that contain both the antecedent and the consequent. Option D is correct because confidence represents the proportion of transactions containing the antecedent that also contain the consequent. Option E is correct because lift = confidence / (probability of consequent). Therefore, lift of 1.2 means confidence is 1.2 times the probability of the consequent. Hence 20% more likely than the baseline. Option B is incorrect because lift, not confidence, captures the relative likelihood compared to the baseline. Option C is incorrect because a lift > 1 suggests a positive correlation, not a negative one.

NEW QUESTION # 111
You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
B. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
C. Create a materialized view containing pre-computed embeddings for all transaction descriptions.
D. Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
E. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

Answer: A,B

Explanation:
Option B is a better approach compared to option A to generate embeddings because its incrementally generate embeddings for new transactions. Option E is also an important approach where if transaction description remains same for the embeddings will not be re-computed. Materialized view is not suited for API integrations like those using Snowflake Cortex. Option D is technically correct, but doesn't address the optimization and cost concerns. Option A Regenerating embeddings for the entire dataset daily is computationally expensive and can quickly lead to high costs, especially with Snowflake Cortex. The best approach is to use caching and compute only for a new transaction description. So correct answer is B and E.

NEW QUESTION # 112
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