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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 2: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
| Topic 3: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Topic 4: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 5: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
| Topic 6: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 7: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
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NEW QUESTION # 30
What functions should a Data Analyst use to run descriptive analytics on a data set? (Select TWO).
Answer: B,D
Explanation:
Descriptive analytics is the process of using historical data to understand "what happened." This typically involves summarizing large datasets into interpretable chunks using central tendency, dispersion, and frequency measures.
AVG (Average) is a cornerstone of descriptive statistics. It provides the arithmetic mean of a numeric column, allowing an analyst to understand the "typical" value within a dataset (e.g., Average Order Value).
APPROX_COUNT_DISTINCT is a descriptive tool used to understand the volume of unique entities within a dataset (e.g., "How many unique customers visited the site?"). Similar to HLL mentioned earlier, this function provides a fast summary of data volume and variety, which is a primary goal of the descriptive phase of analysis.
Evaluating the Options:
* Options A and B (REGR_INTERCEPT and REGR_SLOPE) are used for linear regression. These fall under Predictive Analytics, as they are used to model relationships and predict future outcomes, rather than just describing current data.
* Option C (ROW_NUMBER) is a window function used for data ranking and ordering, but it does not provide a descriptive summary of the dataset's characteristics.
* Options D and E are correct because they provide summary statistics (mean and cardinality) that define the "state" of the data, which is the definition of descriptive analytics.
NEW QUESTION # 31
What distinguishes exploratory ad-hoc analyses from routine data analysis?
Answer: A
Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.
NEW QUESTION # 32
You are preparing a CSV file for ingestion into Snowflake, and you need to ensure that the data types are correctly interpreted. The CSV contains a column named 'transaction_amount' that sometimes contains values with leading zeros (e.g., '00123.45'). You want to load this data into a Snowflake table where 'transaction_amount' is defined as NUMBER(IO, 2). Without modifying the CSV file itself, how can you ensure that the leading zeros are handled correctly during the COPY INTO operation?
Answer: A
Explanation:
Option E is the correct approach. Snowflake implicitly handles leading zeros when casting a VARCHAR column to a NUMBER type using during the COPY INTO operation. This avoids modifying the original CSV file or requiring a separate transformation step after loading. Option A is incorrect, as implicit type conversion might not always work as expected. Option B and C are incorrect because 'STRIP NULL_VALUE' is not relevant in the case of leading zeros. Option D is viable, but less efficient than handling during load time.
NEW QUESTION # 33
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)
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 # 34
You are building a Data Vault model in Snowflake. You have identified a Hub for Customers, a Link table relating Customers to Addresses, and several Satellite tables storing descriptive attributes of both Customers and Addresses. A new business requirement emerges: you need to efficiently query the model to find all Customers who have lived at the same Address as another Customer at any point in time. Which of the following approaches is MOST efficient and scalable for implementing this query in Snowflake, without significantly impacting the Data Vault's core principles?
Answer: A
Explanation:
A materialized view is the most efficient and scalable option. It pre-computes the result, making subsequent queries very fast. Creating a new Link table within the Data Vault would violate its principle of representing facts as they occur. Search optimization service can help, but might not be as efficient as a pre-computed result. A stored procedure iterating through all records is highly inefficient. Adding an array to a Satellite table will cause potential data integrity issues and performance bottlenecks as the array grows, while also deviating from the data vault principles.
NEW QUESTION # 35
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