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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are tasked with analyzing sensor data collected from industrial machines to predict potential failures. The data includes timestamps (EVENT TIMESTAMP), machine IDs ('MACHINE ID'), and various sensor readings ('SENSORI', 'SENSOR? , etc.). You want to use Snowflake's forecasting capabilities to predict when a machine might exceed a critical threshold for 'SENSORI based on historical data'. Which of the following approaches would be the MOST effective in preparing the data and creating the forecasting model?

A. Aggregate the sensor data into hourly averages for each machine ID. Create features such as rolling averages and lagged values of 'SENSORI to capture trends and dependencies. Then, build the forecasting model using these aggregated and engineered features.
B. Create a forecasting model using 'SENSORI ' as the target and use only 'MACHINE_ID' as input features. The forecasting is solely based on Machine ID for predicting sensor data
C. Directly create a forecasting model using the raw sensor data, using 'EVENT _ TIMESTAMP' as the timestamp input and 'SENSORI as the target variable. No data aggregation or feature engineering is required.
D. Perform complex feature engineering by generating polynomial features from all available sensor readings and use a highly complex model such as a deep neural network instead of Snowflake's native forecasting. This ensures maximum accuracy.
E. Use the raw sensor data, but convert 'EVENT _ TIMESTAMP to seconds since the epoch and use that as the timestamp input. No aggregation is required.

Answer: A

Explanation:
Option B is the most effective. Aggregating the data into hourly averages reduces noise and makes the data more manageable for forecasting. Feature engineering, such as creating rolling averages and lagged values, helps capture trends and dependencies in the data, improving the accuracy of the forecasting model. A more refined approach is almost always better than feeding in raw data.

NEW QUESTION # 18
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?

A. Limited external data sources
B. Data format compatibility issues
C. Inability to access marketplace data
D. High data redundancy

Answer: B

Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.

NEW QUESTION # 19
You are working with a table named 'PRODUCT DESCRIPTIONS that contains product descriptions in a 'description' (VARCHAR) column. You need to implement a solution to identify potentially sensitive information within these descriptions, specifically looking for mentions of credit card numbers or social security numbers (SSNs). You want to flag any description that contains either of these patterns. Which of the following Snowflake SQL snippets, leveraging scalar string functions and regular expressions, provides the most efficient and accurate way to achieve this? (Assume that valid credit card numbers are 16 digits and valid SSNs are in the format 'XXX-XX-XXXX'). Select all correct options.

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

Answer: B,D

Explanation:
Options A and C are both correct. Option A uses 'REGEXP_LIKE with two separate regular expressions to search for a 16-digit number (credit card) and a 'XXX-XX-XXXX' pattern (SSN). Option C uses a single 'REGEXP LIKE' function with an alternation to combine both patterns into one regular expression , which is generally more efficient. Option B relies on 'CONTAINS' which performs a simple substring search, not regular expression matching; this will not accurately identify the patterns. Option D uses 'STARTS WITH' which will only identify descriptions that begin with the specified keywords, missing most cases. Option E uses 'LIKE operator for the mentioned words in the sentence that can be 'Credit Card' or 'Social Security Number', but this isn't a search for Credit card numbers or SSNs.

NEW QUESTION # 20
A data analyst observes a sudden and significant drop in sales for a particular product category within a Snowflake database. Initial investigations point to a possible data quality issue. Which of the following steps provides the MOST effective and efficient diagnostic approach using Snowflake features to pinpoint the root cause of the anomaly, focusing on data integrity?

A. Disable all ETL processes and re-ingest the sales data overnight. This will overwrite the potential problematic sales data and resolve the drop in sales.
B. Create a clone of the sales table. Run a full table scan on the clone and perform descriptive statistics on the cloned data to detect statistical outliers without impacting the production environment. Compare with known good statistics.
C. Rebuild the entire product sales table from the raw data source to ensure data consistency, as the drop in sales suggests widespread data corruption. Compare the rebuilt table with the current table.
D. Utilize Snowflake's Time Travel feature to compare the data before and after the suspected anomaly. Query both datasets and perform set operations like EXCEPT or MINUS to identify changed records within the product category. Analyze those changed records for patterns.
E. Analyze query history in Snowflake to identify any recent DML (Data Manipulation Language) operations (e.g., UPDATE, DELETE) performed on the sales table, focusing on operations targeting the affected product category. Correlate these operations with the timeframe of the sales drop.

Answer: D,E

Explanation:
Options A and C are the most effective. A utilizes Time Travel for direct data comparison before and after the incident, allowing for focused analysis on changed records. C investigates DML operations, which could directly explain data changes. Option B is inefficient and disruptive. Option D, while helpful, might not pinpoint the cause of a data corruption issue as fast as A and C. Option E is a poor solution, as it doesn't identify the root cause of the issue and it leads to potential data lost from the transactions between the last successful load and when the ETL processes were disabled.

NEW QUESTION # 21
You need to create a UDF in Snowflake to mask personally identifiable information (PII) in a 'customers' table. The UDF should take a string as input (e.g., email address, phone number) and return a masked version. For email addresses, it should replace everything before the '@' symbol with ' For phone numbers (assuming a simple format like '123-456-7890'), it should mask all but the last four digits with 'X'. Which of the following UDF definitions is MOST efficient and correctly implements this logic using Snowflake's JavaScript UDF capabilities and appropriate error handling?

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

Answer: B

Explanation:
Option D is the most efficient and robust. It uses 'indexOf and regular expressions ('test()') for more reliable string matching, includes error handling using a 'try...catch' block, and correctly handles the different masking scenarios. The regex ensures that the phone number format is strictly validated before masking. Options A and B lack error handling and use less precise matching techniques (e.g., 'includes' which could lead to unintended behavior). Option C is similar to option D but utilizes javascript includes which is not reliable as indexof in finding exact position. Option E is a Python UDF, which, while valid, would be less performant than javascript UDF as specified in question.

NEW QUESTION # 22
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