FREE PDF QUIZ DSA-C03 - NEWEST SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM EXAM CERTIFICATION COST

Free PDF Quiz DSA-C03 - Newest SnowPro Advanced: Data Scientist Certification Exam Exam Certification Cost

Free PDF Quiz DSA-C03 - Newest SnowPro Advanced: Data Scientist Certification Exam Exam Certification Cost

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?

  • A. Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
  • B. Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.
  • C. Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:
  • D. Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.
  • E. Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:

Answer: C

Explanation:
Option B is the most effective and efficient. Using Snowflake's 'SAMPLE clause with 'BERNOULLI' sampling is optimized for large tables. Creating a new table with 'CREATE OR REPLACE TABLE AS SELECT ... UNION ALL avoids modifying the original data and combines the down-sampled majority class with the minority class. 'SAMPLE BERNOULLI (1)' samples 1% of the data, which can be adjusted to achieve the desired balance. Option A is slow, Option C less efficient than Snowflake sampling for initial prep, Option D samples the entire majority class and does not downsample, and Option E is generally less efficient than Snowflake SQL for large-scale sampling.


NEW QUESTION # 148
A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

  • A. Option C
  • B. Option A
  • C. Option E
  • D. Option B
  • E. Option D

Answer: C

Explanation:
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. The standard error of the mean is calculated as the population standard deviation divided by the square root of the sample size. In this case, we are estimating the standard deviation from a sample of sample means, hence using 'STDDEV_SAMP', and the sample size here is the number of days, i.e. 30.


NEW QUESTION # 149
You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

  • A. Option A
  • B. Option C
  • C. Option E
  • D. Option B
  • E. Option D

Answer: A

Explanation:
Option A correctly calculates precision and recall. Precision is calculated as True Positives / (True Positives + False Positives), and Recall is calculated as True Positives / (True Positives + False Negatives). The query in option A directly implements these formulas, where 'PREDICTED CHURN = 1 AND CHURN = 1' represents True Positives. Option B and E calculates accuracy. Option C calculates correlation. Option D calculates Precision and Recall for the negative class (non-churn).


NEW QUESTION # 150
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

  • A. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
  • B. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
  • C. Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
  • D. Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.
  • E. Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.

Answer: C,E

Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.


NEW QUESTION # 151
A data scientist is building a churn prediction model using Snowflake data'. They want to load a large dataset (50 million rows) from a Snowflake table 'customer_data' into a Pandas DataFrame for feature engineering. They are using the Snowflake Python connector. Given the code snippet below and considering performance and memory usage, which approach would be the most efficient for loading the data into the Pandas DataFrame? Assume you have a properly configured connection and cursor 'cur'. Furthermore, assume that the 'customer id' column is the primary key and uniquely identifies each customer. You are also aware that network bandwidth limitations exist within your environment. ```python import snowflake.connector import pandas as pd # Assume conn and cur are already initialized # conn = snowflake.connector.connect(...) # cur = conn.cursor() query = "SELECT FROM customer data```

  • A. ```python cur.execute(query) df = pd.DataFrame(cur.fetchall(), columns=[col[0] for col in cur.description])
  • B. ```python with conn.cursor(snowflake.connector.DictCursor) as cur: cur.execute(query) df = pd.DataFrame(cur.fetchall())
  • C. ```python import snowflake.connector import pandas as pd import pyarrow import pyarrow.parquet # Enable Arrow result format conn.cursor().execute("ALTER SESSION SET PYTHON USE ARROW RESULT FORMAT-TRUE") cur.execute(query) df =
  • D. ```python cur.execute(query) df = pd.read_sql(query, conn)
  • E. ```python cur.execute(query) results = cur.fetchmany(size=1000000) df_list = 0 while results: df_list.append(pd.DataFrame(results, for col in cur.description])) results = cur.fetchmany(size=1000000) df = pd.concat(df_list, ignore_index=True)

Answer: C

Explanation:
Option E, utilizing Arrow result format and , is the most efficient for large datasets. Snowflake's Arrow integration leverages columnar data transfer, significantly speeding up data retrieval compared to row-based methods (fetchall, fetchmany). Also its optimized for Pandas. Options A, B, C, and D retrieve data row by row (or in chunks) and construct the DataFrame iteratively, which is slower and consumes more memory. The DictCursor in D, while useful, doesn't fundamentally change the data transfer efficiency compared to using the Arrow format.


NEW QUESTION # 152
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