Reliable Snowflake DSA-C03 Braindumps Ppt | Detailed DSA-C03 Answers

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A. Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
B. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
C. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
D. Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
E. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.

Answer: A

Explanation:
Option C is the most efficient. 'execute_stream' allows you to fetch data in chunks, preventing out-of-memory errors with large tables. You can perform transformations on each chunk, reducing the memory footprint. Loading the entire table at once (A) is inefficient for large datasets. Using ssnowsqr (B) or 'COPY INTO' (E) adds an extra step of unloading and reloading, increasing the time taken. Creating a Snowflake view (D) is a good approach for pre-processing but might not fully address memory issues during the final load into Pandas, especially if the view still contains a large amount of data.

NEW QUESTION # 35
A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A. An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT
B. Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
C. A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
D. An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):
E. A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')

Answer: A

Explanation:
The correct answer is E. Since we're comparing the means of two independent samples (Campaign A and Campaign B) and the population standard deviations are unknown, an independent samples t-test is appropriate. Because the problem stated that the variances are unequal, Welch's t-test provides a more accurate p-value and confidence intervals. The Snowflake function handles independent samples and the 'VAR_EQUAL=FALSE' parameter specifies that the variances should not be assumed to be equal. The other options are incorrect because they use inappropriate tests given the problem conditions. The z-test is not appropriate because the population standard deviations are unknown. A paired t-test is for related samples. A one sample test is to test one mean against a constant not another mean.

NEW QUESTION # 36
You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?

Answer: E

Explanation:
Option B is the most correct. It correctly calculates the median and mean for the specified customer segments using 'agg()' with .alias(y to name the resulting aggregate columns, and then retrieves the values using . This approach correctly handles the aggregation and retrieval of the calculated median and mean values. Option A uses which although technically works, is less readable than the aliased approach. The method provides similar performance benefits to the method with simpler syntax, as you retrieve only the first row of the DataFrame. 'toLocallterator' is a performant way to get local access to the result of an aggregation function when a small number of rows are expected. Option C fails because it attempts to use the aggregate directly without materializing the value. The comparison between using .agg(), .collect(), .first(), and .toLocallterator() demonstrates performance tuning knowledge.

NEW QUESTION # 37
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: B,E

Explanation:
Options A and B are the most effective. A identifies data drift by comparing feature distributions, indicating potential changes in the input data. B monitors performance metrics and triggers alerts based on predefined thresholds. C is too manual and inefficient. D isn't appropriate, re-running model training would produce a new model not identifying degradation of current one. E is premature; further investigation is necessary. Assessing the performance is import after significant drops found for retraining of the model with latest data.

NEW QUESTION # 38
You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called 'CUSTOMER DATA. You want to assess the model's calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

A. Create a calibration curve (also known as a reliability diagram) by binning the predicted CLTV values, calculating the average predicted CLTV and the average actual CLTV within each bin, and plotting these averages against each other.
B. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.
C. Calculate the R-squared score on a hold-out test set to assess the proportion of variance in the actual CLTV explained by the model.
D. Calculate the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a hold-out test set to quantify the overall prediction accuracy.
E. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.

Answer: A

Explanation:
Option B is the most suitable technique for assessing calibration. A calibration curve directly visualizes the relationship between predicted and actual values, allowing you to see if the model is systematically over- or under-predicting CLTV for different ranges of predicted values. Options A, C, and D are useful for assessing overall accuracy and model fit but do not directly address calibration. MAE and RMSE (A) measure overall error magnitude. Residual analysis (C) can reveal problems with model assumptions. R-squared (D) measures the explained variance, not calibration. Option E measures whether two samples follow the same distribution, however, it would not be most suitable for assessing calibration of your CLTV regression model.

NEW QUESTION # 39
......

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RELIABLE SNOWFLAKE DSA-C03 BRAINDUMPS PPT | DETAILED DSA-C03 ANSWERS

Reliable Snowflake DSA-C03 Braindumps Ppt | Detailed DSA-C03 Answers