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| Section | Weight | Objectives |
|---|---|---|
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
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NEW QUESTION # 48
A Data Analyst is working with three tables:
Which query would return a list of all brokers, a count of the customers each broker has. and the total order amount of their customers (as shown below)?
Answer: D
Explanation:
To achieve the desired result, an analyst must understand the fundamental behavior of different JOIN types within Snowflake and how they affect the retention of records from the "left" or primary table. The goal here is to list all brokers, even those who have zero customers (like "Drew") or customers with zero orders (like
"Debby").
In SQL, an INNER JOIN only returns rows when there is a match in both tables. If we were to use an INNER JOIN between BROKER and CUSTOMER, Drew would be excluded from the results because he has no associated records in the CUSTOMER table. Similarly, an INNER JOIN with the ORDERS table would exclude any broker whose customers haven't placed an order.
Evaluating the Join Logic:
* Option C is the correct solution because it utilizes a chain of LEFT JOINs. A LEFT JOIN (or LEFT OUTER JOIN) ensures that every record from the left table (BROKER) is preserved in the result set. If no matching record exists in the joined table (CUSTOMER or ORDERS), Snowflake populates the columns with NULL. This is why "Drew" appears with a CUST_COUNT of 0 and "Debby" appears with a NULL for the total order amount.
* Option A fails because it uses an INNER JOIN for the CUSTOMER table, which would immediately filter out "Drew."
* Option B and Option D fail because they use INNER JOINs at different stages of the query, which would strip away brokers or customers that do not have matching order activity.
Additionally, the query correctly uses COUNT(DISTINCT c.customer_id) to ensure that customers are not double-counted if they have multiple orders, and GROUP BY 1 (referencing b.broker_name) to aggregate the data at the broker level. This pattern is essential for accurate Data Analysis in Snowflake when dealing with
"optional" relationships in a star or snowflake schema.
NEW QUESTION # 49
You have Parquet files containing customer order data in an external stage. You need to create a secure view in Snowflake that filters the data based on user roles. Only users with the 'SALES REP' role should see orders from their assigned region, while 'MANAGER' roles should see all data'. Assuming you have set up appropriate role hierarchies, how would you implement this using row-level security policies with Parquet external tables?
Answer: E
Explanation:
Row Access Policies (RAP) are designed for Row Level Security, and they are applied to tables or views. Since the data is in an external table accessed through a view, the policy should be attached to the secure view. The ' function is used to determine the user's current role within the policy's logic. Secure Views protect the underlying data and policy logic from unauthorized access. Delegating ownership to 'ACCOUNTADMIW is not strictly required but a good security practice. Masking policies aren't the correct choice here as it is used to hide data from the user.
NEW QUESTION # 50
A Data Analyst needs a sample of 10 rows from a table FCT_SALES that has billions of rows. Which commands can be used to accomplish this? (Select TWO).
Answer: A,B
Explanation:
When dealing with massive datasets containing billions of rows, efficiency is paramount. Snowflake provides two primary ways to retrieve a small subset of data: Sampling and Result Set Limiting.
The SAMPLE clause is used to return a subset of rows based on a specific count or percentage. The correct syntax for specifying a fixed number of rows is SAMPLE (<number> ROWS). This is highly effective for exploratory data analysis where you need to see the shape of the data without the overhead of scanning the entire table. It is important to note that SAMPLE does not guarantee a truly random distribution unless specified, but it is much faster than a full table scan.
The TOP <n> or LIMIT <n> clauses are used to restrict the number of rows returned in the output. The TOP
<n> syntax is commonly used in many SQL dialects and is fully supported in Snowflake to return the first "n" rows the query engine encounters. While TOP is often used with an ORDER BY clause to get the highest or lowest values, using it alone on a massive table like FCT_SALES is a standard way to quickly peak at the data.
Evaluating the Options:
* Option A is incorrect syntax; the ROWS keyword and parentheses are required for a fixed count.
* Option B is Correct; it uses the standard Snowflake SAMPLE syntax for a specific row count.
* Option C and Option E are incorrect because ROWS is not a valid keyword within a TOP clause in Snowflake.
* Option D is Correct; it uses the standard TOP <n> syntax to limit the result set.
By using these methods, a Data Analyst ensures they are not wasting compute resources (Credits) by pulling billions of unnecessary rows into the UI or a local environment.
NEW QUESTION # 51
You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?
Answer: E
Explanation:
Option D is the correct answer. When consuming data from a Snowflake Marketplace data share, you do not need to grant privileges to the provider on your internal data (Option A is incorrect). Option B is incorrect because creating views and acessing tables requires proper schema names. Option C is incorrect because outbound shares are not needed to consume marketplace data and access control is managed within the consumer account. Option E is incorrect because enabling access control policies on a warehouse is irrelevant to the task of creating a secure view that joins internal data with shared data.
NEW QUESTION # 52
You have a Snowflake table named 'LOG DATA' with a "VARIANT' column called 'log_message" storing application logs in JSON format. The 'log_message' structure contains a nested JSON object called 'context' , which may or may not be present in every log entry. The 'context' object itself contains several key-value pairs, including potentially a 'user_id', a 'session_id' , and/or a 'request_id' You need to extract all the unique combination of 'user _ id' , and 'request_id' that exist across the dataset, accounting for instances where the 'context' object or any of the individual IDs are missing (represented as NULL). The goal is to ensure no errors are thrown due to missing fields. Select all the options which would achieve this outcome.
Answer: C,E
Explanation:
Options B and E are both valid solutions. Option B Uses 'TRY TO_STRING' function. If value is not present and is NULL, then automatically it will be converted to Null without any failure. Option E Uses 'COALESCE will handle any missing 'context object or missing IDs within the 'context' object without throwing an error. If any of the three values are NULL then it would be converted to string 'NULL'. Why other options are not entirely correct Option A This is not a correct answer, as a result in case NULL values encountered, this will result in failures. Option C & D This is a valid solution, however, since NVL and IFF functions are slower compared to TRY _ TO STRING and COALESCE, this result in slow processing. So, its not entirely correct answer.
NEW QUESTION # 53
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