DSA-C03最も有効な質問と解答で勉強

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Snowflake DSA-C03 試験シラバストピック:

Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題：

1. You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

A) Create a UDF that computes the purchase frequency for a single user and apply it to all customers.
B) Calculate the average purchase frequency across all customers using and group by 'customer_id'.
C) Use the window function to divide customers into quantiles based on their total purchase count.
D) Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
E) Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.

2. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

3. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

4. You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A) Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
B) Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.
C) Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.
D) Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.
E) Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.

5. You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?

A) Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes.
B) Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift.
C) Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay.
D) Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.
E) Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold.

質問と回答：