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

NEW QUESTION # 89
You are tasked with estimating the 95% confidence interval for the median annual income of Snowflake customers. Due to the non-normal distribution of incomes and a relatively small sample size (n=50), you decide to use bootstrapping. You have a Snowflake table named 'customer_income' with a column 'annual_income'. Which of the following SQL code snippets, when correctly implemented within a Python script interacting with Snowflake, would most accurately achieve this using bootstrapping with 1000 resamples and properly calculate the confidence interval?

Answer: C

Explanation:
Option A is the correct answer. It accurately implements bootstrapping by: (1) Resampling with replacement from the original data. (2) Calculating the median of each resample. (3) Computing the 2.5th and 97.5th percentiles of the bootstrap medians to obtain the 95% confidence interval. Option B calculates the mean instead of the median, and uses 'random.sample' without replacement, which is incorrect for bootstrapping. Option C doesn't resample at all, just calculates the mean of the original data repeatedly. Option D calculates the mean instead of the median. Option E calculates 90% confidence interval instead of 95%.

NEW QUESTION # 90
You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
B. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
C. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
D. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
E. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.

Answer: A

Explanation:
Option D, leveraging Snowflake's native support for Streamlit, is the most appropriate and efficient approach. Streamlit allows you to build interactive web applications directly within the notebook, querying data directly from Snowflake and using Streamlit's built-in plotting capabilities (or integrating with other Python visualization libraries). This avoids pulling large amounts of data into the notebook's environment, which is crucial for large datasets. Option A is inefficient due to the data transfer overhead and limited interactivity. Option B can work but is not as streamlined as using Streamlit within the Snowflake environment. Option C will create static plots only. Option E is overly complex and less efficient than using Streamlit.

NEW QUESTION # 91
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. 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.
B. 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.
C. 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.
D. 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.
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: A

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 # 92
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 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
B. 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.
C. 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.
D. The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
E. The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.

Answer: C,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 # 93
You are developing a Python stored procedure in Snowflake to train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?

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

Answer: C

Explanation:
Passing the column names as a VARIANT array and using parameterized queries is the safest and most efficient approach. This avoids SQL injection vulnerabilities, as the column names are treated as data rather than code. It also allows Snowflake to optimize the query execution plan. Options A and C are vulnerable to SQL injection. Option D doesn't address the core problem of dynamically specifying columns and security. Option E introduces an extra layer of abstraction (the view) but doesn't inherently solve the dynamic column specification or SQL injection risks if the view definition is itself dynamically constructed.

NEW QUESTION # 94
......

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DSA-C03 VALID LEARNING MATERIALS & DSA-C03 NEW TEST CAMP

DSA-C03 Valid Learning Materials & DSA-C03 New Test Camp