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Quiz High Hit-Rate Snowflake - DSA-C03 - Valid SnowPro Advanced: Data Scientist Certification Exam Dumps Demo

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The DSA-C03 exam dumps are the ideal study material for quick and complete DSA-C03 exam preparation. The real and top-notch Snowflake DSA-C03 exam questions are being offered in three different formats. These formats are Snowflake DSA-C03 PDF Dumps Files, desktop practice test software, and web-based practice test software.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

Answer: B

Explanation:
Option A is correct. It properly casts the timestamp string to a TIMESTAMP data type using 'CAST(activity_timestamp AS TIMESTAMP) , extracts the hour using 'EXTRACT(HOUR FROM ... y , and applies the sine and cosine transformations to create the cyclical features. Options B, C, and D might contain syntax errors or incorrect functions. Using SUBSTRING to extract can be prone to errors as it doesn't perform data validation. Option E also works but it uses a MOD function which is redundant. Therefore, it is less preferable to Option A.

NEW QUESTION # 248
A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

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

Answer: D

Explanation:
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. The standard error of the mean is calculated as the population standard deviation divided by the square root of the sample size. In this case, we are estimating the standard deviation from a sample of sample means, hence using 'STDDEV_SAMP', and the sample size here is the number of days, i.e. 30.

NEW QUESTION # 249
You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

A. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
B. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
C. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
D. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.
E. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.

Answer: E

Explanation:
The most appropriate approach is to extract the data and perform the confidence interval calculations outside of the stored procedure using a dedicated statistical environment. Options A and D are less scalable and efficient within the stored procedure. Option B provides insufficient information. Option E is not feasible for dynamic calculation based on changing data.

NEW QUESTION # 250
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. 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.
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. Utilize a SQL-based affinity propagation method directly within Snowflake. This removes the need for feature scaling and specialized hardware.

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 # 251
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. 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.
B. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
C. 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.
D. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
E. 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.

Answer: C,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 # 252
......

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