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[General] DSA-C03 Valid Dumps Questions & Test DSA-C03 Dump

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【General】 DSA-C03 Valid Dumps Questions & Test DSA-C03 Dump

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q168-Q173):NEW QUESTION # 168
You are building a model to predict loan defaults using data stored in Snowflake. As part of your feature engineering process within a Snowflake Notebook, you need to handle missing values in several columns: 'annual _ income', and You want to use a combination of imputation strategies: replace missing values with the median, 'annual_income' with the mean, and with a constant value of 0.5. You are leveraging the Snowpark DataFrame API. Which of the following code snippets correctly implements this imputation strategy?

  • A. Option D
  • B. Option E
  • C. Option A
  • D. Option C
  • E. Option B
Answer: A,C
Explanation:
Options A and D both correctly implement the specified imputation strategy. Option A uses 'fillna' method with respective median and mean values, calculated using 'approxQuantile' and mean for missing values.Option B uses 'na.fill' which is used in Spark, and Snowflake is not compatible. Option C calculates the median and mean, but incorrectly tries to use the local Python variables inside F.lit() functions, which are executed on the Snowflake server. Option D uses loops for column selection. Option E tries to apply a literal value within a dictionary being used to fill the missing values. This is not correct, and it's important to ensure that a correct implementation is used.

NEW QUESTION # 169
You are analyzing sensor data collected from industrial machines, which includes temperature readings. You need to identify machines with unusually high temperature variance compared to their peers. You have a table named 'sensor _ readings' with columns 'machine_id', 'timestamp', and 'temperature'. Which of the following SQL queries will help you identify machines with a temperature variance that is significantly higher than the average temperature variance across all machines? Assume 'significantly higher' means more than two standard deviations above the mean variance.

  • A. Option E
  • B. Option A
  • C. Option D
  • D. Option C
  • E. Option B
Answer: B
Explanation:
The correct answer is A. This query first calculates the variance for each machine using a CTE (Common Table Expression). Then, it calculates the average variance and standard deviation of variances across all machines. Finally, it selects the machine IDs where the variance is more than two standard deviations above the average variance. Option B is incorrect because it tries to calculate aggregate functions within the HAVING clause without proper grouping. Option C uses a JOIN which is inappropriate in this scenario. Option D is incorrect because the window functions will not return the correct aggregate values. Option E is syntactically incorrect. QUALIFY clause should have partition BY statement.

NEW QUESTION # 170
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. Keeping all columns as is and providing access to Data Scientists without any changes, relying on role based security access controls only.
  • B. 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.
  • 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. 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'.
Answer: A
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 # 171
A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

  • A. Option E
  • B. Option A
  • C. Option D
  • D. Option C
  • E. Option B
Answer: A
Explanation:
Both options B and D achieve the desired label encoding and handle NULL values correctly. Option B creates a new table based on the transformed dataset, which is a common approach for preserving the original data. Option D directly updates the existing table which could be appropriate depending on the team and security structure.

NEW QUESTION # 172
You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)
  • A. Remove outlier data points to improve overall model performance.
  • B. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
  • C. Increase the regularization strength in your model.
  • D. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
  • E. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
Answer: D,E
Explanation:
A sinusoidal pattern in the residuals indicates that the model is not adequately capturing the seasonal patterns in the data. Incorporating lagged features (Option B) allows the model to learn from past sales trends. A Box-Cox transformation (Option C) can help stabilize the variance and improve the model's fit. Increasing regularization (Option A) or removing outliers (Option D) might help in some cases, but they are not the primary solutions for a sinusoidal pattern. Linear regression models are unlikely to capture sinusoidal patterns, so Option E is wrong.

NEW QUESTION # 173
......
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