Snowflake SnowPro Advanced DSA-C03

퍼펙트한 서비스 제공

승진이나 연봉인상을 꿈꾸면 승진과 연봉인상을 시켜주는 회사에 능력을 과시해야 합니다. IT인증시험은 국제적으로 승인해주는 자격증을 취득하는 시험입니다. 시험을 패스하여 자격증을 취득하면 회사에서 꽃길만 걷게 될것입니다. DSA-C03인증시험덤프 덤프구매전 한국어 온라인 상담서비스부터 구매후 덤프 무료 업데이트버전 제공, DSA-C03인증시험 덤프 불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. SnowPro Advanced: Data Scientist Certification Exam인증시험 덤프는 인기덤프인데 지금까지 DSA-C03덤프를 구매한후 불합격으로 인해 환불신청하신 분은 아직 한분도 없었습니다.

DSA-C03시험은 최근 제일 인기있는 인증시험입니다. IT업계에 종사하시는 분들은 자격증취득으로 자신의 가치를 업그레이드할수 있습니다. SnowPro Advanced: Data Scientist Certification Exam 시험은 유용한 IT자격증을 취득할수 있는 시험중의 한과목입니다. Snowflake SnowPro Advanced 최신버전 덤프는 여러분들이 한방에 시험에서 통과하도록 도와드립니다. DSA-C03덤프를 공부하는 과정은 IT지식을 더 많이 배워가는 과정입니다. DSA-C03시험대비뿐만아니라 많은 지식을 배워드릴수 있는 덤프를 공부하고 시험패스하세요.

최신 업데이트버전 DSA-C03덤프

저희는 2,3일에 한번씩 DSA-C03덤프자료가 업데이트 가능한지 체크하고 있습니다. SnowPro Advanced: Data Scientist Certification Exam덤프가 업데이트된다면 업데이트된 버전을 고객님 구매시 사용한 메일주소로 발송해드립니다. DSA-C03 덤프 업데이트서비스는 구매일로부터 1년내에 유효함으로 1년이 지나면 DSA-C03덤프 업데이트서비스가 자동으로 종료됩니다. SnowPro Advanced: Data Scientist Certification Exam덤프 무료 업데이트 서비스를 제공해드림으로 고객님께서 구매하신 DSA-C03덤프 유효기간을 최대한 연장해드립니다.

시험준비시간 최소화

IT업계 엘리트한 강사들이 퍼펙트한 DSA-C03시험응시 SnowPro Advanced: Data Scientist Certification Exam덤프문제집을 제작하여 디테일한 DSA-C03문제와 답으로 여러분이 아주 간단히 SnowPro Advanced: Data Scientist Certification Exam시험응시를 패스할 수 있도록 최선을 다하고 있습니다.덤프구매후 2,3일 정도만 공부하시면 바로 시험보셔도 되기에 가장 짧은 시간을 투자하여 시험에서 패스할수 있습니다.

최신 SnowPro Advanced DSA-C03 무료샘플문제:

1. A data scientist is tasked with identifying customer segments for a new marketing campaign using transaction data stored in Snowflake. The transaction data includes features like transaction amount, frequency, recency, and product category. Which unsupervised learning algorithm would be MOST appropriate for this task, considering scalability and Snowflake's data processing capabilities, and what preprocessing steps are crucial before applying the algorithm?

A) Hierarchical clustering, using the complete linkage method and Euclidean distance. No preprocessing is necessary, as hierarchical clustering can handle raw data.
B) K-Means clustering, after standardizing numerical features (transaction amount, frequency, recency) and using one-hot encoding for product category. This is highly scalable within Snowflake using UDFs and SQL.
C) Principal Component Analysis (PCA) followed by K-Means. This reduces dimensionality and then clusters, improving the visualization of the cluster.
D) K-Means clustering, after applying min-max scaling to numerical features and converting categorical features to numerical representation. The optimal 'k' (number of clusters) should be determined using the elbow method or silhouette analysis.
E) DBSCAN, using raw data without any scaling or encoding. The algorithm's density-based nature will automatically handle the varying scales of the features.

2. A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A) Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
B) Calculated as 'applicant_income I loan_amount'.
C) Calculated as 'loan_amount I applicant_age' .
D) Calculated as 'applicant_age / applicant_income'.
E) Calculated as 'applicant_age applicant_age'.

3. You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

A) Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.
B) Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.
C) Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.
D) Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.
E) Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.

4. You are tasked with identifying Personally Identifiable Information (PII) within a Snowflake table named 'customer data'. This table contains various columns, some of which may contain sensitive information like email addresses and phone numbers. You want to use Snowflake's data governance features to tag these columns appropriately. Which of the following approaches is the MOST effective and secure way to automatically identify and tag potential PII columns with the 'PII CLASSIFIED tag in your Snowflake environment, ensuring minimal manual intervention and optimal accuracy?

A) Use Snowflake's built-in classification feature with a pre-defined sensitivity category to identify potential PII columns. Associate a masking policy that redacts the data, and apply a tag 'PII_CLASSIFIED' via automated tagging to the columns identified as containing PII.
B) Manually inspect each column in the 'customer_data' table and apply the 'PII_CLASSIFIED' tag to columns that appear to contain PII based on their names and a small sample of data.
C) Create a custom Snowpark for Python UDF that uses regular expressions to analyze the data in each column and apply the 'PII_CLASSIFIED tag if a match is found. Schedule this UDF to run periodically using Snowflake Tasks.
D) Export the 'customer_data' to a staging area in cloud storage, use a third-party data discovery tool to scan for PII, and then manually apply the "PII_CLASSIFIED' tag to the corresponding columns in Snowflake based on the tool's findings.
E) Write a SQL script to query the 'INFORMATION SCHEMA.COLUMNS' view, identify columns with names containing keywords like 'email' or 'phone', and then apply the 'PII_CLASSIFIED tag to those columns.

5. You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A) Select a threshold of 0.5, as this is a common default threshold for binary classification problems.
B) Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.
C) Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.
D) Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.
E) Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.

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