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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation and Data Modeling | 22%-30% | - Transform data using SQL
|
| Topic 2: Data Ingestion and Data Preparation | 15%-20% | - Use a collection system to retrieve data
|
| Topic 3: Data Analysis | 30%-32% | - Perform advanced analytics using SQL
|
| Topic 4: Data Presentation and Data Visualization | 28%-29% | - Integrate with BI tools
|
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NEW QUESTION # 14
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?
Answer: A,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 # 15
A financial analyst is using Snowflake to forecast stock prices based on historical data'. They have a table named 'STOCK PRICES with columns 'TRADE DATE (DATE) and 'CLOSING PRICE (NUMBER). They want to implement a custom moving average calculation using window functions to smooth out short-term fluctuations and identify trends. Specifically, they need to calculate a 7-day weighted moving average, where the most recent day has the highest weight and the weights decrease linearly. Which SQL statement correctly implements this weighted moving average calculation?
Answer: C
Explanation:
Option E is the correct answer because it accurately calculates the 7-day weighted moving average with linearly decreasing weights. It assigns weights from 7 (most recent) down to 1 (oldest) within the 7-day window. The weight calculation '(7 - ROW_NUMBER() OVER (ORDER BY TRADE DATE DESC) + 1)' ensures the most recent date has a weight of 7, and the weights decrease linearly to 1. The sum of the weighted closing prices is then divided by the sum of the weights to get the weighted average. Other options are incorrect because they either calculate a simple moving average, apply incorrect weights, or have syntactic errors. Option B and D's row_number() is ordered ascending, resulting in the oldest data point having the highest weight.
NEW QUESTION # 16
There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?
Answer: A
Explanation:
In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near- instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.
However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see.
Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.
Evaluating the Options:
* Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.
* Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.
* Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.
* Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.
NEW QUESTION # 17
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?
Answer: B
Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.
NEW QUESTION # 18
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?
Answer: A
Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.
NEW QUESTION # 19
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