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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
Consider you are working on a credit risk scoring model using Snowflake. You have a table 'credit data' with the following schema: 'customer id', 'age', 'income', 'credit_score', 'loan_amount', 'loan_duration', 'defaulted'. You want to create several new features using Snowflake SQL to improve your model. Which combination of the following SQL statements will successfully create features for age groups, income-to-loan ratio, and interaction between credit score and loan amount using SQL in Snowflake? Choose all that apply.

Answer: B,E

Explanation:
Options D and E are correct. Option D creates a VIEW that dynamically calculates all three features without modifying the underlying table. A view is the correct and recommended usage. Option E creates a new table with all the features including the new engineered features. Option A creates a column and updates it, but this is inefficient compared to creating the feature directly in a single SELECT statement (Option E). B createa a temporary table but does not contain all three features. Option C, it only addresses the interaction feature, not age_group or income to loan ratio.

NEW QUESTION # 56
You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A. Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
B. Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
C. Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
D. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
E. Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.

Answer: C,E

Explanation:
Options A and B are the recommended approaches. Option A leverages Snowflake UDFs for inference, which minimizes data transfer and leverages Snowflake's compute. Option B, using Snowpark, provides a more seamless integration with Snowflake's distributed processing capabilities. Option C introduces external dependencies and latency. Option D requires managing and maintaining a separate microservice and data transfer and Option E is not viable.

NEW QUESTION # 57
A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

A. The CLT is not applicable because the population distribution (call durations) is heavily skewed.
B. The CLT is applicable, and the sample mean will converge to the population median.
C. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
D. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
E. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.

Answer: C,D

Explanation:
The Central Limit Theorem (CLT) states that the distribution of sample means will be approximately normally distributed, regardless of the shape of the population distribution, as long as the sample size is large enough. While the rule of thumb is typically n > 30, the skewness of the original population distribution can influence how large the 'large enough' sample size needs to be. In this scenario, since the call durations follow an exponential distribution (which is skewed), a reasonably large sample size will still allow the CLT to be applicable, and the sample means' distribution will approach normality. The CLT ensures convergence toward normality in the distribution of sample means, not convergence of the sample mean to the population median.

NEW QUESTION # 58
You are working with a Snowflake table 'CUSTOMER DATA containing customer information for a marketing campaign. The table includes columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS, 'CITY, 'STATE, ZIP CODE, 'COUNTRY, 'PURCHASE HISTORY, 'CLICKSTREAM DATA, and 'OBSOLETE COLUMN'. You need to prepare this data for a machine learning model focused on predicting customer churn. Which of the following strategies and Snowpark Python code snippets would be MOST efficient and appropriate for removing irrelevant fields and handling potentially sensitive personal information while adhering to data governance policies? Assume data governance requires removing personally identifiable information (PII) that isn't strictly necessary for the churn model.

A. Dropping 'FIRST NAME, UST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS', 'CITY, 'STATE', ZIP CODE, 'COUNTRY and 'OBSOLETE_COLUMN' columns directly using 'LAST_NAME', 'EMAIL', 'PHONE_NUMBER', 'ADDRESS', 'CITY', 'STATE', 'ZIP_CODE', 'COUNTRY', without any further consideration.
B. Dropping columns 'OBSOLETE_COLUMN' directly. Then, for PII columns ('FIRST_NAME, 'LAST_NAME, 'EMAIL', 'PHONE_NUMBER, 'ADDRESS', 'CITY', 'STATE' , , 'COUNTRY), create a separate table with anonymized or aggregated data for analysis unrelated to the churn model. Use Keep all PII columns but encrypt them using Snowflake's built-in encryption features to comply with data governance before building the model. Drop 'OBSOLETE COLUMN'.
C. Drop 'OBSOLETE_COLUMN'. For columns like and 'LAST_NAME' , consider aggregating into a single 'FULL_NAME feature if needed for some downstream task. Apply hashing or tokenization techniques to sensitive PII columns like and 'PHONE NUMBER using Snowpark UDFs, depending on the model's requirements. Drop columns like 'ADDRESS, 'CITY, 'STATE, ZIP_CODE, 'COUNTRY as they likely do not contribute to churn prediction. Example hashing function:
D. Keeping all columns as is and providing access to Data Scientists without any changes, relying on role based security access controls only.

Answer: D

Explanation:
Option D is the most comprehensive and adheres to best practices. It identifies and removes truly irrelevant columns ('OBSOLETE_COLUMN', and location details), handles PII appropriately using hashing and tokenization (or aggregation), and leverages Snowpark UDFs for custom data transformations. Options A is too simplistic and doesn't consider data governance. Option B is better than A, but more complex than needed if the data is not needed elsewhere. Option C doesn't address the principle of minimizing data exposure. Option E is unacceptable from a data governance and security perspective. The example code demonstrates how to register a UDF for hashing email addresses.

NEW QUESTION # 59
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

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

Answer: D

Explanation:
Option C is the best prompt because it directly instructs the LLM to act as a product expert and provide only the feature description, minimizing extraneous conversation or information. Options A and B lack specific instructions, potentially leading to verbose responses. Option D includes all product details in the prompt, which might overwhelm the LLM. Option E tries to fetch a specific feature description, but the SQL is incorrect. Correct SQL will increase token usage and may not lead to a concise response.

NEW QUESTION # 60
......

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UPDATED SNOWFLAKE DSA-C03 EXAM QUESTIONS - FAST TRACK TO GET SUCCESS

Updated Snowflake DSA-C03 Exam Questions - Fast Track To Get Success