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Free PDF Quiz Snowflake - DSA-C03 - Marvelous Test SnowPro Advanced: Data Scientist Certification Exam Sample Questions

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

A. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
B. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
C. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.
D. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
E. Use the role within the pipeline script to create and grant all necessary privileges.

Answer: B

Explanation:
The principle of least privilege dictates that the pipeline should only have the minimum necessary privileges to perform its tasks. Creating a custom role with only the required privileges and granting it to a dedicated service account is the most secure approach. Using 'ACCOUNTADMIN' (Option A) or 'SYSADMIN' (Option C) grants excessive privileges. Hardcoding credentials (Option D) is a major security vulnerability. Granting 'OWNERSHIP (Option E) is generally not necessary and grants excessive control. This follows the principle of least privilege which is essential for secure Snowflake deployments. A dedicated role ensures that the pipeline cannot inadvertently perform actions outside of its intended scope.

NEW QUESTION # 47
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. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
B. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
C. 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.
D. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
E. 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.

Answer: C

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 # 48
You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?

A. Implement a global exception handler within the UDTF that catches all exceptions, logs the error message to a Snowflake table, and returns a default forecast value when a network error occurs. Ensure the error logging table exists and has sufficient write permissions for the UDTF.
B. Use the 'try...except' block specifically around the code that makes the API call. Within the 'except block, catch specific network-related exceptions (e.g., requests.exceptions.RequestException', 'socket.timeout'). Log the error to a Snowflake stage using the 'logging' module and retry the API call a limited number of times with exponential backoff.
C. Configure Snowflake's network policies to allow outbound network access from the UDTF to the specific REST API endpoint. This will eliminate the network connectivity issues and prevent the UDTF from failing.
D. Before making the API call, check the network connectivity using the 'ping' command. If the ping fails, skip the API call and return a default forecast value. This prevents the UDTF from attempting to connect to an unavailable endpoint.
E. Use a combination of retry mechanisms (like the tenacity library) with exponential backoff around the API call. If the retry fails after a predefined number of attempts, then return pre-computed data or use a simplified model as the UDTF's output.

Answer: B,E

Explanation:
Options B and E are the MOST appropriate for handling network errors. Using a 'try...except block (B) specifically targets the API call and allows for handling network-related exceptions gracefully. Logging the error to a Snowflake stage provides valuable debugging information. Retry with exponential backoff increases the chances of success during transient network issues. Option E improves upon option B with external and maintained libraries such as tenacity and returning a model output, not just a single value, when the error is recoverable or the data is missing. Option A, a global exception handler, is too broad and might mask other errors. Option C is a necessary prerequisite but does not address intermittent connectivity issues. Option D's 'ping' command is not reliable for determining API availability and might introduce unnecessary delays or false negatives. A complete end-to-end, complete solution must focus on addressing all aspects of code and execution.

NEW QUESTION # 49
You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

A. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used
B. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
C. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.
D. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
E. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.

Answer: E

Explanation:
Option E is the most suitable because it explicitly addresses the temporal dependency and prevents data leakage by creating sequential, non-overlapping folds based on 'TRANSACTION DATE. Options A and D rely on potentially incorrect assumptions by Snowflake about time series data and are unlikely to provide the correct cross-validation folds. Option B can introduce leakage because it treats dates as categorical variables and performs random assignment. Option C performs the cross validation entirely outside of Snowflake, which negates the benefits of Snowflake's scalability and data proximity.

NEW QUESTION # 50
You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

A. Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.
B. DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
C. K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
D. Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
E. Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.

Answer: C,E

Explanation:
Sequence clustering is appropriate for identifying navigation paths because it considers the order of URLs visited within a session. Using Snowflake's LISTAGG function allows for creating the required sequential data. K-Means clustering can also be effective if relevant features are engineered from the URL data (e.g., frequency of visiting specific domains). Association rule mining is less suitable for identifying navigation paths as it focuses on co-occurrence rather than sequence. PCA followed by hierarchical clustering and DBSCAN are not well-suited for identifying sequential navigation paths from clickstream data. Option 'A' is incorrect because association rule mining directly on raw URL data is unlikely to be effective without prior sequence extraction. Option 'D' and 'E' are not suitable for this type of analysis.

NEW QUESTION # 51
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