Prep and try our DSA-C03 valid and latest training questions & answers

Snowflake DSA-C03 Exam Overview:

Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

2. You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

3. You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A)

B)

C)

D)

E)

4. A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A) 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.
B) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
C) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
D) 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.
E) 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.

5. You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

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

Solutions: