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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

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

Answer: E

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.

NEW QUESTION # 33
A Snowflake data analyst needs to create a secure view called "masked customer data' based on an existing table named 'customer data'. The requirement is to mask the 'email' column for all users except those with the "DATA ADMIN' role. Also, only users with the 'ANALYST' role should be able to query any data from the view. The masking policy 'email_mask' has already been created. Which of the following sequence of commands correctly implements this requirement?

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

Answer: D

Explanation:
Option C correctly implements the requirements. First, a 'SECURE VIEW' is created to ensure data security. Then, the 'ALTER VIEW' command correctly applies the masking policy to the column of the view. Finally, 'GRANT SELECT ensures that only the 'ANALYST role can query data from the view. Option B attempts to alter the underlying table directly which isn't the intention, and uses an invalid command 'MODIFY COLUMN'. Option A does not create a secure view. Option D uses incorrect syntax 'MODIFY COLUMN' for altering view. Option E try to alter the masked_customer_data, which is not a TABLE instead it is a View.

NEW QUESTION # 34
A data pipeline is failing intermittently, with the error logs indicating 'Insufficient compute resources'. You are tasked with collecting data to diagnose the root cause. What combination of Snowflake features and data collection strategies would be MOST effective in identifying if warehouse auto-scaling or query performance is the primary contributor to the issue?

A. Monitor the Snowflake warehouse resource monitor metrics (e.g., QUEUED LOAD BYTES, EXECUTION TIME) using the Snowflake web interface or view, focusing on periods coinciding with pipeline failures.
B. Increase the warehouse size significantly and observe if the pipeline failures cease.
C. Create a new, larger warehouse and migrate the pipeline to it without analyzing the current warehouse's performance.
D. Analyze the average query execution time for all queries run by the pipeline using the QUERY_HISTORY view and compare it to the warehouse's average execution time. Additionally, check for queries spilling to local disk (using WAREHOUSE LOAD) during failure periods.
E. Collect only error logs generated by the data pipeline to understand underlying problem.

Answer: A,D

Explanation:
Options A and C provide a targeted approach. A allows tracking warehouse resource usage during the pipeline failures, identifying if the warehouse is reaching its limits. C helps identify slow-running queries contributing to resource exhaustion and detects queries using excessive local disk, indicating potential optimization opportunities. Increasing the warehouse size without analysis (B & D) is not cost-effective. Collecting only error logs (E) is not sufficient for a comprehensive diagnostic approach. It is too narrow and may not expose warehouse load issues and bottlenecks.

NEW QUESTION # 35
You are tasked with building a dashboard that visualizes website traffic data stored in Snowflake. The data includes daily unique visitors, bounce rate, and average session duration. The business stakeholders want to understand the correlation between these metrics. They also want to identify any outliers or anomalies. Which chart type is BEST suited for identifying correlation and outliers in this dataset?

A. A bar chart comparing the average values of each metric.
B. A histogram showing the distribution of individual Metrics.
C. A line chart showing each metric over time.
D. A scatter plot matrix showing the pairwise relationships between all metrics.
E. A pie chart showing the percentage contribution of each metric to the total.

Answer: D

Explanation:
A scatter plot matrix displays the pairwise relationships between multiple variables. This allows for easy identification of correlations (positive, negative, or none) and outliers in the data. Line charts are good for showing trends over time, but not for directly visualizing correlations between different metrics. Bar charts compare average values, and pie charts show proportions. Histograms helps to show single distribution only.

NEW QUESTION # 36
You are tasked with identifying PII (Personally Identifiable Information) within several tables in your Snowflake data warehouse before granting access to a new analytics team. You have a database called 'CUSTOMER DATA with tables 'CUSTOMERS' , 'ADDRESSES' , and 'ORDERS'. Which of the following SQL queries, leveraging Snowflake's information schema, would be the most efficient and least intrusive method to discover potential PII columns, assuming you have a naming convention where PII columns often contain terms like 'EMAIL', 'PHONE', 'SSN', or 'NAME'?

Answer: C

Explanation:
The correct answer is E. It leverages the 'information_schema.columns' view, which is a standard and efficient way to retrieve metadata in Snowflake. using 'table_schema = 'CUSTOMER_DATA" and 'table_name IN ('CUSTOMERS', 'ADDRESSES', 'ORDERS')' filters the results to the specific database and tables of interest, making it more efficient than scanning all columns in the account or using table_catalog'. ensures case-insensitive matching. Options A and D uses either wrong schema or wrong like. Option B and C uses account_usage, that can introduce delay to data availability since that is for billing and monitoring and also does not filter by table names

NEW QUESTION # 37
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