How PrepAwayETE DSA-C03 Exam Practice Questions Can Help You in Exam Preparation?
How PrepAwayETE DSA-C03 Exam Practice Questions Can Help You in Exam Preparation?
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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q84-Q89):
NEW QUESTION # 84
You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?
- A. Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.
- B. Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
- C. Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.
- D. Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
- E. Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.
Answer: B
Explanation:
Option B is the most efficient and scalable approach. Partitioning by product category allows for faster querying of specific categories. Clustering on the timestamp column ensures that recent predictions are quickly accessible. A composite key of product category and timestamp provides uniqueness. Option A lacks any optimization for querying. Option C can lead to performance issues with large JSON documents and querying specific values within the VARIANT. Option D introduces latency due to external stage access, and external tables are generally slower for frequent queries compared to native Snowflake tables. Option E introduces external dependency and network latency, which is generally not preferred if a native Snowflake solution is possible.
NEW QUESTION # 85
You're building a linear regression model in Snowflake to predict house prices. You have the following features: 'square_footage', 'number of bedrooms', 'location id', and 'year built'. 'location id' is a categorical variable representing different neighborhoods. You suspect that the relationship between 'square footage' and 'price' might differ based on the 'location id'. Which of the following approaches in Snowflake are BEST suited to explore and model this potential interaction effect?
- A. Use the 'QUALIFY clause in Snowflake SQL to filter the data based on 'location_id' before calculating regression coefficients. This is incorrect approach.
- B. Create interaction terms by adding 'square_footage' and one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
- C. Fit separate linear regression models for each unique 'location_id', using 'square_footage', 'number_of_bedrooms', and 'year_built' as independent variables.
- D. Create interaction terms by multiplying 'square_footage' with one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
- E. Apply a power transformation to 'square_footage' before including it in the linear regression model. This correct, but only to one variable.
Answer: D
Explanation:
Creating interaction terms by multiplying 'square_footage' with one-hot encoded columns from 'location_id' allows the model to estimate different slopes for 'square_footage' for each location. This directly models the interaction effect. Fitting separate models might be computationally expensive and does not allow for sharing of information across locations. The QUALIFY clause is used for filtering and not directly relevant to modeling interactions. A power transformation only affects 'square_footage' and not the interaction effect. Adding instead of multiplying will not create an interaction.
NEW QUESTION # 86
You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?
- A. Option D
- B. Option E
- C. Option B
- D. Option A
- E. Option C
Answer: A
Explanation:
The 'snowflake_cortex.sentiment' function returns a VARIANT containing the sentiment score and sentiment label. To extract the sentiment score as a float, you need to access the 'sentiment_score' field and cast it to FLOAT or NUMBER data type using Option D does this correctly, using the Snowflake's preferred casting syntax.Option A return type is incorrect, where it returns the Variant instead of FLOAT. Option B return type is correct but it doesnt cast the result to Float, which is not correct syntax as result is VARIANT. Option C is incorrect because of TO_NUMBER function. Option E result is the SENTIMENT Label instead sentiment score.
NEW QUESTION # 87
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. Calculated as 'loan_amount I applicant_age' .
- B. Calculated as 'applicant_age applicant_age'.
- C. Calculated as 'applicant_age / applicant_income'.
- D. Calculated as 'applicant_income I loan_amount'.
- E. 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)
Answer: A,D,E
Explanation:
The best combination provides diverse perspectives on financial stability. directly reflects the applicant's ability to cover the loan with their income. represents the loan burden relative to the applicant's age and can expose risk in younger, less established applicants. provides the most comprehensive view, including existing debt obligations from external data. "age_squared' and are less directly informative about repayment ability. They could potentially capture non-linear relationships, but 'age_squareff is more likely to introduce overfitting. relies on an external data source, making it a powerful, but potentially more complex, feature to implement.
NEW QUESTION # 88
You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:
- A. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
- B. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.
- C. Do not scale the data, as K-means is robust to differences in feature scales.
- D. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
- E. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .
Answer: A,B,E
Explanation:
K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.
NEW QUESTION # 89
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