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| Section | Weight | Objectives |
|---|---|---|
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| 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% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
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NEW QUESTION # 30
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?
Answer: D
Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.
NEW QUESTION # 31
What functions should a Data Analyst use to run descriptive analytics on a data set? (Select TWO).
Answer: B,E
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 # 32
How does understanding and analyzing the query execution plan contribute to query optimization in Snowflake?
Answer: A
Explanation:
Analyzing the query execution plan assists in understanding query processing steps, identifying bottlenecks, and optimizing query performance by streamlining execution steps.
NEW QUESTION # 33
How does enriching data through Snowflake Marketplace benefit data analysis? (Select all that apply)
Answer: A,B
Explanation:
Enriching data through the marketplace expands data sources for correlation and enhances overall data quality.
NEW QUESTION # 34
What actions can be taken when responding to processing failures in data processing solutions?
(Select all that apply)
Answer: A,B,D
Explanation:
Responding to processing failures involves logging error details, identifying the root cause, and resuming processing from the failure point.
NEW QUESTION # 35
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