Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03무료 덤프문제 풀어보기

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A. Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.

B. Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.

C. Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.

D. Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.

E. Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'

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B. Option E

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A. Utilize Snowflake's external functions to call a pre-existing sentiment analysis API (e.g., Google Cloud Natural Language API or AWS Comprehend) passing the review text and storing the returned sentiment score and category. Ensure proper API key management and network configuration.

B. Create a series of Snowflake SQL queries utilizing complex string matching and keyword analysis to determine sentiment based on predefined lexicons. Categories are assigned through CASE statements.

C. Use a Python UDF (User-Defined Function) with a pre-trained sentiment analysis library (e.g., NLTK or spaCy) to calculate the sentiment score and categorize it. Deploy the UDF in Snowflake and apply it to the 'review_text' column.

D. Use a Snowflake procedure that reads all 'review_text' data, transfers data outside of Snowflake to an external server running sentiment analysis software, and then writes results back into a new table.

E. Create a Snowpark Python DataFrame from the Snowflake table, use a sentiment analysis library within the Snowpark environment, categorize the sentiments, and then save the resulting DataFrame back to Snowflake as a new table.

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. 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.

C. 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.

D. 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.

E. 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.

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B. Option E

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D. Option A

E. Option B

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B. Option E

C. Option D

D. Option A

E. Option B

A. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.

B. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.

C. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.

D. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.

E. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.

A. Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.

B. Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.

C. Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.

D. Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.

E. Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.

A. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.

B. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.

C. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.

D. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.

E. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.

A. Use 'window.partitionBy('customer_id').orderBy('transaction_date').rowsBetween(-6, Window.currentRow)' within a 'select' statement and handle any missing dates using 'fillna()' after calculating the rolling average.

B. Use a stored procedure in SQL to iterate over each customer, calculate the rolling average using a cursor and conditional logic for handling missing dates.

C. Use'window.partitionBy('customer_id').orderBy('transaction_date').rangeBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with a date range table joined to the transactions, filling in missing days before calculating the rolling average with 'transaction_amount' set to 0 for the inserted days.

D. Use a simple followed by a UDF to calculate the rolling average. Fill in missing dates manually within the UDF.

E. Use a Snowpark Pandas UDF to calculate the rolling average for each customer after collecting all transactions for that customer into a Pandas DataFrame. Handle missing dates using Pandas functionality.

A. Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.

B. Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.

C. Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.

D. Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.

E. Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.

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