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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q278-Q283):

NEW QUESTION # 278
You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A. Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
B. Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
C. Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
D. Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
E. Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.

Answer: A,B,E

Explanation:
Options A, B, and C are the best choices for low-latency, high-throughput fraud detection. Option A: Using Snowflake Tasks with the 'WHEN SYSTEM$STREAM HAS DATA()' clause ensures that tasks only run when there is new data in the stream, enabling incremental processing and reducing unnecessary computations. Option B: Dynamic Tables automatically update as the underlying data changes, providing continuously transformed features. Increasing the 'WAREHOUSE SIZE ensures enough compute resources are available. Option C: Snowpark ML UDFs allow you to score incoming transactions in near real-time, leveraging Snowflake's vectorized engine for fast performance. Option D introduces a delay of one hour for model retraining, which is not ideal for a real-time system. Furthermore, regularly scheduling retrain tasks and rerunning them when data is available from stream is not most efficient processing paradigm. While option E is relevant, the question focuses on the transformation, model scoring and data processing parts of a real time data sciecne pipeline, for which A, B, and C are more directly connected.

NEW QUESTION # 279
You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

A. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.
B. Use the role within the pipeline script to create and grant all necessary privileges.
C. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
D. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
E. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.

Answer: D

Explanation:
The principle of least privilege dictates that the pipeline should only have the minimum necessary privileges to perform its tasks. Creating a custom role with only the required privileges and granting it to a dedicated service account is the most secure approach. Using 'ACCOUNTADMIN' (Option A) or 'SYSADMIN' (Option C) grants excessive privileges. Hardcoding credentials (Option D) is a major security vulnerability. Granting 'OWNERSHIP (Option E) is generally not necessary and grants excessive control. This follows the principle of least privilege which is essential for secure Snowflake deployments. A dedicated role ensures that the pipeline cannot inadvertently perform actions outside of its intended scope.

NEW QUESTION # 280
You are using the NetworkX library in Snowpark Python to analyze social network data stored in a Snowflake table named 'USER CONNECTIONS', which has columns 'USER ID' and 'CONNECTED USER representing connections between users. You want to find the users with the highest 'betweenness centrality' to identify influential nodes in the network. Which Snowpark Python code snippet would correctly calculate and display the top 5 users with the highest betweenness centrality?

Answer: B

Explanation:
Option A is the most efficient and correct approach. It leverages for directly creating the graph from a Pandas DataFrame (converted from the Snowpark DataFrame), calculates betweenness centrality using NetworkX, creates a Pandas DataFrame for results, and then sorts and displays the top 5 users. Options B iterates through the rows, which is less efficient, and attempts to create a Snowpark DataFrame from the betweenness dictionary, which isn't the most efficient output mechanism in this context. Option C is almost correct but uses 'nlargest' which is also valid. Option D uses which is slower and less efficient than Option E is very close but includes the parameter which is unnecessary for this specific operation since it's the initial creation of the betweenness df and the index isn't crucial to be reset. So while it functions it is redundant.

NEW QUESTION # 281
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. Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
B. Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
C. Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
D. 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.
E. Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.

Answer: D

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 # 282
You're analyzing the performance of two different AIB testing variants of an advertisement. You've collected the following data over a period of one week: Variant A: 1000 impressions, 50 conversions Variant B: 1100 impressions, 66 conversions Which of the following statements are TRUE regarding confidence intervals and statistical significance in this scenario?

A. A narrower confidence interval for the difference in conversion rates implies a higher degree of certainty about the estimated difference.
B. If the 95% confidence interval for the conversion rate of Variant A is entirely above the 95% confidence interval for the conversion rate of Variant B, then Variant A is statistically better than Variant B.
C. Increasing the sample size (number of impressions for each variant) will generally widen the confidence interval, making it more likely to contain zero.
D. Constructing a 95% confidence interval for the difference in conversion rates between Variant B and Variant A will allow you to assess if there is a statistically significant difference at the 5% significance level. If the confidence interval contains zero, there is no statistically significant difference.
E. Calculating separate confidence intervals for conversion rates A and B, and noting overlap, is an invalid method to infer statistical significance. One must construct confidence interval for the difference in means.

Answer: A,D,E

Explanation:
Options A, B, and E are correct. Option A correctly explains the relationship between confidence intervals and statistical significance at a given significance level. Option B is correct because narrower interval correctly infers higher certainty. Option E is correct since you need a single measure of difference not each variable measured separately. Option C is incorrect: increasing the sample size will generally narrow the confidence interval, making it less likely to contain zero. Option D is incorrect. You cannot conclude statistical superiority by comparing if one confidence interval is entirely above other. You must construct a difference interval to compare. There is more to overlap than just that.

NEW QUESTION # 283
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Snowflake DSA-C03 Pass4sure Exam Prep & Exam DSA-C03 PDF

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q278-Q283):

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