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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q179-Q184):

NEW QUESTION # 179
You're analyzing a sales dataset in Snowflake. The dataset contains 'customer id', 'product id', 'sale date', and 'sale amount'. You've noticed that some 'sale_amount' values are negative, which is illogical. You also suspect duplicate records, indicated by identical 'customer_id', 'product_id', and 'sale_date'. Additionally, a business rule dictates that sale amounts must be less than $10,000. Which combination of Snowflake SQL statements is the MOST efficient and accurate way to address these data quality issues?

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

Answer: E

Explanation:
Option E correctly handles all specified data quality issues. It replaces negative sale amounts with 0, enforces the business rule (sale_amount < 10000), and removes duplicate records based on 'customer_id', 'product_id', and 'sale_date' using the QUALIFY clause and ROW_NUMBER() window function, selecting only the first occurrence of each duplicate set. Options A and D do not handle negative sales amount correctly, Option B uses IFF(sale_amount < O, O, sale_amount) but do not handle boundary values of sale_amount < 10000, Option C considers sale amount between 0 and 10000.

NEW QUESTION # 180
Your team is building a data pipeline to ingest data from a REST API that returns JSON payloads. Due to API rate limits, you need to implement a backoff strategy to avoid overwhelming the API. You are using Python and the 'requests' library for data ingestion. Which code snippet BEST demonstrates a robust backoff strategy with exponential backoff and jitter?

Answer: E

Explanation:
Option B provides the most robust backoff strategy. It implements exponential backoff (wait time increases exponentially with each retry) and adds jitter (a random amount of time) to the wait time to avoid synchronized retries from multiple clients. also handles error codes correctly. Option A doesn't have a backoff strategy. Option C has a simple retry mechanism but lacks exponential backoff and a limit on retries. Option D lacks error handling and retry logic. Option E uses recursion, which is generally not recommended and does not feature exponential backoff.

NEW QUESTION # 181
You are analyzing website traffic data in Snowflake. The 'web_events' table contains 'event_timestamp' (TIMESTAMP N T Z), 'user_id', and 'page_url'. You discover that many 'event_timestamp' values are significantly skewed towards the future (e.g., a year ahead), likely due to incorrect device clocks. You want to correct these skewed timestamps by assuming the majority of events are valid and calculating a time drift. Which of the following strategies using Snowflake functionality would be MOST efficient and accurate for correcting these timestamps?

A. Calculate the average 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
B. Calculate the mode of the 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
C. Calculate the median 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the median. Subtract this difference from the future skewed events to correct them.
D. Calculate the average 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the average. Subtract this difference from the future skewed events to correct them.
E. Calculate the median 'event_timestamp' for each 'user_id' and subtract the overall median 'event_timestamp' from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.

Answer: C

Explanation:
Option D provides the most robust approach. Using the median minimizes the impact of outliers (future-dated timestamps). Calculating the difference between each event timestamp and the overall median timestamp isolates the 'time_drift' for each record, which is then subtracted from each future skewed events. Option A uses median for each user, which is unneccesary. Options B and E are vulnerable to outliers (the very problem we're trying to solve). Option C, while conceptually interesting, isn't directly supported as a native aggregate function for timestamps in most SQL dialects, including Snowflake, without custom user-defined functions (UDFs), making it less efficient and potentially less accurate.

NEW QUESTION # 182
You are tasked with loading data from an S3 bucket into a Snowflake table named 'SALES DATA'. The data is in CSV format, compressed with gzip, and contains a header row The S3 bucket requires AWS IAM role authentication. The 'SALES DATA' table already exists, and you want to use a named stage for this ingestion process. Which of the following steps are necessary to successfully load the data, minimizing administrative overhead?

A. Create an external function to read the data from S3 and then insert it into the table, as Snowflake cannot directly read gzipped CSV files from S3.
B. Ensure the S3 bucket has public read access; Snowflake's COPY INTO command will handle decompression and data loading without further configuration.
C. Create a Snowflake stage object that references the storage integration, the S3 bucket URL, and specifies the file format (CSV with gzip compression and header skip). Use the 'COPY INTO' command referencing the stage.
D. Create a new IAM role in AWS with access to the S3 bucket, then create a Snowflake storage integration object referencing that role's ARN and the S3 bucket's URL.
E. Grant usage privilege on the storage integration to the role performing the data load. Ensure the user loading data has access to the Snowflake stage and the ' INSERT privilege on the table.

Answer: C,D,E

Explanation:
Options A, B, and D are correct. Option A: Snowflake needs an IAM role and storage integration to access the S3 bucket securely. Option B: A stage object simplifies the COPY INTO command and handles file format details. Option D: Correct permissions are required for the data load to succeed. Option C is incorrect because Snowflake can directly read gzipped CSV files from S3 when configured correctly. Option E is incorrect because granting public read access to the S3 bucket is a security risk and is not the best practice; using IAM roles provides controlled and secure access.

NEW QUESTION # 183
You're building a dashboard to monitor the performance of various marketing campaigns. The data resides in Snowflake, and you're using a BI tool that supports direct query The table has columns: 'CAMPAIGN ID, 'DATE, 'IMPRESSIONS', 'CLICKS, 'SPEND , and You need to create a calculated field in the BI tool representing the Cost Per Conversion (CPC), but you want to optimize query performance and avoid division by zero errors. Assume 'SPEND' and 'CONVERSIONS' are both numeric columns. Which SQL expression, suitable for use in a direct query BI tool, is the MOST performant and robust way to calculate CPC, avoiding zero conversion issues?

Answer: E

Explanation:
The ' DIV0' function is specifically designed by Snowflake to handle division by zero gracefully, returning NULL. It's the most concise and performant way to achieve the desired result. 'NULLIF(CONVERSIONS, 0)' is also correct way, but DIVO is more accurate for Snowflake environment. 'CASE WHEN' and 'IFF are functionally equivalent in this scenario, but is shorter to write. ZEROIFNULL' will return 0 when input is null which won't solve the zero conversion issues. Furthermore ZEROIFNULL' is not a valid Snowflake function.

NEW QUESTION # 184
......

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