시험대비자료는 덤프가 최고

처음으로 자격증에 도전하시는 분들이 많을것이라 믿습니다.우선 시험센터나 인증사 사이트에서 고객님께서 취득하려는 자격증이 어느 시험을 보셔야 취득이 가능한지 확인하셔야 합니다.그리고 시험시간,출제범위,시험문항수와 같은 SnowPro Advanced: Data Scientist Certification Exam시험정보에 대해 잘 체크하신후 그 시험코드와 동일한 코드로 되어있는 덤프를 구매하셔서 시험공부를 하시면 됩니다.DSA-C03덤프구매전 사이트에서 일부분 문제를 다운받아 덤프유효성을 확인하셔도 좋습니다.저희 사이트의 영원히 변치않는 취지는 될수있는 한 해드릴수 있는데까지 DSA-C03시험 응시자 분들께 편리를 가져다 드리는것입니다. 응시자 여러분들이 시험을 우수한 성적으로 합격할수 있도록 적중율 높은 덤프를 제공해드릴것을 약속드립니다.

덤프유효기간을 최대한 연장

DSA-C03덤프를 구매하시면 1년무료 업데이트 서비스를 제공해드립니다.덤프제작팀은 거의 매일 모든 덤프가 업데이트 가능한지 체크하고 있는데 업데이트되면 고객님께서 덤프구매시 사용한 메일주소에 따끈따끈한 가장 최신 업데이트된 DSA-C03덤프자료를 발송해드립니다.고객님께서 구매하신 덤프의 유효기간을 최대한 연장해드리기 위해 최선을 다하고 있지만 혹시라도 SnowPro Advanced: Data Scientist Certification Exam시험문제가 변경되어 시험에서 불합격 받으시고 덤프비용을 환불받는다면 업데이트 서비스는 자동으로 종료됩니다.

IT 업계의 선두자로서 저희의 목표는 IT인증시험에 참가하는 모든 분들께 도움을 제공해드리는 것입니다. 이 목표를 달성하기 위해 저희의 전문가들은 시간이 지날수록 쌓이는 경험과 노하우로 IT자격증시험 응시자분들을 지원하고 있습니다.덤프제작팀의 엘리트들은 최선을 다하여 근년래 출제된 SnowPro Advanced: Data Scientist Certification Exam 시험문제의 출제경향을 분석하고 정리하여 가장 적중율 높은 DSA-C03시험대비 자료를 제작하였습니다.이와 같은 피타는 노력으로 만들어진 DSA-C03 덤프는 이미 많은 분들을 도와 DSA-C03시험을 패스하여 자격증을 손에 넣게 해드립니다.

데모문제 다운로드

자격증의 필요성

IT업계에 종사하시는 분께 있어서 국제인증 자격증이 없다는 것은 좀 심각한 일이 아닌가 싶습니다. 그만큼 자격증이 취직이거나 연봉협상, 승진, 이직 등에 큰 영향을 끼치고 있습니다. DSA-C03시험을 패스하여 자격증을 취득하시면 고객님께 많은 이로운 점을 가져다 드릴수 있습니다. 이렇게 중요한 시험인만큼 고객님께서도 시험에 관해 검색하다 저희 사이트까지 찾아오게 되었을것입니다. DSA-C03덤프를 공부하여 시험을 보는것은 고객님의 가장 현명한 선택이 될것입니다.덤프에 있는 문제를 마스터하시면 SnowPro Advanced: Data Scientist Certification Exam시험에서 합격할수 있습니다.구매전이거나 구매후 문제가 있으시면 온라인서비스나 메일상담으로 의문점을 보내주세요. 친절한 한국어 서비스로 고객님의 문의점을 풀어드립니다.

최신 SnowPro Advanced DSA-C03 무료샘플문제:

1. 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) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain 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) 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.
E) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.

2. You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A) 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
B) 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
C) 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
D) 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
E) 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.

3. You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

A) Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
B) Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
C) Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
D) Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
E) Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.

4. A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

A) Option E
B) Option D
C) Option C
D) Option B
E) Option A

5. A marketing team uses Snowflake to store customer purchase data'. They want to segment customers based on their spending habits using a derived feature called The 'PURCHASES' table has columns 'customer id' (IN T), 'purchase_date' (DATE), and 'purchase_amount' (NUMBER). The team needs a way to handle situations where a customer might have missing months (no purchases in a particular month). They want to impute a 0 spend for those months before calculating the average. Which approach provides the most accurate and robust calculation, especially when considering users with sparse purchase history?

A) Calculate the total spend for each customer and divide by the number of months since their first purchase: / DATEDlFF(month, CURRENT DATE()) GROUP BY customer_id'.
B) Create a view containing all months for each customer, left join with the 'PURCHASES' table, impute 0 for null 'purchase_amounts values, and then calculate the average spend. Requires creating a helper table for all the month.
C) Calculate the average monthly spend directly from the 'PURCHASES' table without accounting for missing months: 'AVG(purchase_amount) GROUP BY customer_id, date_trunc('month',
D) Calculate the average spend only for customers with purchases in every month of the year. Ignore other customers in the analysis.
E) Use a window function to calculate the average spend over a fixed window of the last 3 months, ignoring missing months in the calculation.

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