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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Build and Troubleshoot Advanced SQL Queries	20–25%	- Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Prepare and Load Data	15–20%	- File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)

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

NEW QUESTION # 32
Which statement accurately describes the usage of materialized views in data analysis?

A. Materialized views are restricted to storing small subsets of data.
B. Materialized views are only accessible through stored procedures.
C. Materialized views update in real-time, reflecting instantaneous changes in the database.
D. They offer a precomputed, persisted snapshot of data, improving query performance.

Answer: D

Explanation:
Materialized views provide precomputed snapshots of data, enhancing query performance by reducing computation overhead.

NEW QUESTION # 33
A Data Analyst executes a complex query. Which query will allow the Analyst to access the results a second time?

A. SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));
B. DESC RESULT LAST_QUERY_ID();
C. SELECT * FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY());
D. SELECT LAST_QUERY_ID(-1);

Answer: A

Explanation:
Snowflake provides a powerful feature called Result Caching. When a query is executed, the results are persisted for 24 hours. To access the result set of a previously executed query without re-running the heavy computation (and thus saving credits), a Data Analyst can use the RESULT_SCAN table function.
The function RESULT_SCAN requires a Query ID as its input. The most common way to programmatically retrieve the ID of the most recently executed statement in the current session is by using the LAST_QUERY_ID() function. By wrapping this in the TABLE() operator, Snowflake treats the cached results as a virtual table that can be queried, filtered, or even joined with other tables.
Evaluating the Options:
* Option B is incorrect because QUERY_HISTORY returns metadata about queries (like start time, user, and status) but does not return the actual data records produced by those queries.
* Option C is incorrect as DESC (Describe) is used to see the columns and data types of an object, not to fetch its row data.
* Option D is incorrect; while LAST_QUERY_ID(-1) might successfully retrieve a string representing a previous ID in some contexts, it does not actually "access the results" or display the data.
* Option A is the 100% correct syntax. It is the standard method used by Data Analysts to perform post- processing on a large result set or to recover a result set if the worksheet was accidentally cleared. This is a key part of the Data Analysis domain, specifically regarding performance optimization and the utilization of Snowflake's unique caching layers.

NEW QUESTION # 34
You are tasked with identifying potential fraud in a financial transactions table named 'transactions'. The table includes 'transaction id' , account id', 'transaction date', and 'transaction_amount'. You need to flag transactions where the transaction amount is significantly higher than the average transaction amount for that account within the last 7 days, specifically, three standard deviations higher. Which analytic function or combination of functions would best accomplish this, while optimizing for performance? (Select all that apply)

A. Use a user-defined function (UDF) to iterate through each account's transactions, calculate the average and standard deviation for the last 7 days, and flag transactions exceeding the threshold.
B. Calculate the median transaction amount using the 'MEDIAN()' window function over a 7-day window. Flag transactions where the amount exceeds the median by a predefined percentage (e.g., 200%).
C. Calculate the average and standard deviation of transaction amounts for each account using 'AVG()' and window functions over a 7-day window, and then use a 'CASE' statement to flag transactions that exceed the threshold (average + 3 standard deviation).
D. Calculate the average transaction amount for each account over the entire dataset. Flag transactions exceeding three times this global average.
E. Utilize the ' NTILE()' window function to divide transactions into quantiles based on amount within each account's 7-day window. Flag transactions falling into the highest quantile as potentially fraudulent.

Answer: C

Explanation:
Option A is the best approach for accurately identifying outliers using statistical measures. Calculating both the average ('AVG()') and standard deviation using window functions over a 7-day window allows for a dynamic threshold that adapts to each account's transaction behavior. Combining this with a 'CASE statement allows you to flag those transactions which are above three standard deviations from the average. Option B is less efficient than using Snowflake's built-in analytic functions. Options C, D, and E do not correctly identify outliers based on the specified criteria. The MEDIAN can hide many outliers, and the total dataset average and NTILE are less accurate.

NEW QUESTION # 35
In data modeling for BI requirements, when is it preferable to use a flattened data set instead of a data model?

A. For scenarios necessitating extensive data transformations
B. For quick and simple data exploration
C. For complex data analysis needs
D. For situations requiring high data normalization

Answer: B

Explanation:
Flattened data sets are suitable for quick and simple data exploration due to their simplified structure, facilitating easy access and analysis.

NEW QUESTION # 36
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 histogram showing the distribution of individual Metrics.
B. A line chart showing each metric over time.
C. A scatter plot matrix showing the pairwise relationships between all metrics.
D. A pie chart showing the percentage contribution of each metric to the total.
E. A bar chart comparing the average values of each metric.

Answer: C

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 # 37
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