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| Section | Weight | Objectives |
|---|---|---|
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| 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 - Align outputs with business requirements - Data visualization and reporting |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
| Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
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NEW QUESTION # 63
Consider the following SQL query:
Analyzing the Query Profile, you observe that the 'WHERE' clause is not effectively filtering the data'. Which of the following actions could improve performance, assuming the 'order date' column is NOT currently clustered or indexed?
Answer: A,C
Explanation:
Clustering on 'order_date' will physically organize the data on disk according to the date, allowing Snowflake to efficiently retrieve the relevant data based on the 'WHERE clause. Creating a materialized view pre-computes the result set based on the filter, so the query would only need to retrieve the top 10 from that smaller dataset. Secondary indexes are not supported in Snowflake. Partitioning is not a feature in Snowflake. Increasing the warehouse size might help with processing power, but it won't directly improve data access efficiency.
NEW QUESTION # 64
You have a Snowpipe configured to load CSV files from an AWS S3 bucket into a Snowflake table. The CSV files are compressed using GZIP. You've noticed that Snowpipe is occasionally failing with the error 'Incorrect number of columns in file'. This issue is intermittent and affects different files. Your team has confirmed that the source data schema should be consistent. What combination of actions provides the most likely and efficient solution to address this intermittent column count mismatch issue?
Answer: A,E
Explanation:
Setting *ERROR ON COLUMN COUNT MISMATCH' to FALSE allows the pipe to continue without halting on such errors. However, this approach will leave behind bad records. Carriage return issues can occur, which affect the column count when ingesting data. If there are carriage return characters inside the CSV fields, this will be misinterpreted as delimiters. Option A might help if headers are present and consistent, but is less likely the root cause of an intermittent column count mismatch. Option C is unlikely to be a primary cause of column count issues as GZIP decompression is generally reliable. Option E is a workaround, but less efficient than correctly configuring the CSV parsing.
NEW QUESTION # 65
A Data Analyst needs to generate a graphic that will identify any natural clusters in a data set containing details about completed orders. Which Snowsight chart should be used?
Answer: B
Explanation:
In the realm of Data Presentation and Data Visualization, selecting the appropriate chart type is critical for revealing the underlying structure of data. When an analyst is tasked with identifying "natural clusters," they are looking for groups of data points that share similar characteristics across two or more dimensions.
A Scatter Plot is the primary visualization tool for this purpose. By plotting two numeric variables (e.g., ORDER_AMOUNT on the X-axis and CUSTOMER_LOYALTY_SCORE on the Y-axis), the scatter plot allows the human eye to immediately detect density. If the data points form distinct "clouds" or groupings, these represent the clusters the analyst is searching for. In Snowsight, scatter plots are highly effective for exploratory data analysis, as they allow analysts to visualize the relationship between variables and spot outliers that may skew statistical summaries.
Evaluating the Options:
* Option A is incorrect because Line Charts are designed to show trends over time (sequential data).
They connect points in a specific order, which would obscure clustering logic by forcing a temporal connection between unrelated data points.
* Option B is incorrect because a Heat Grid (or Heatmap) is best used for comparing categories across two dimensions using color intensity. While it can show high-density areas, it aggregates data into
"bins" or cells, which can mask the fine-grained distribution required to see natural, point-based clusters.
* Option C is incorrect because Bar Charts are used for comparing discrete categories or showing distributions within a single dimension. They do not effectively show the correlation between two continuous variables needed for cluster identification.
* Option D is the 100% correct answer. The Scatter Plot is the standard analytical graphic for cluster detection and correlation analysis within the Snowflake Snowsight interface, providing the necessary granularity to see how individual orders or customers group together in a multi-dimensional space.
NEW QUESTION # 66
What distinguishes exploratory ad-hoc analyses from routine analysis?
Answer: C
Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.
NEW QUESTION # 67
How do secure views enhance data analysis practices?
Answer: B
Explanation:
Secure views offer enhanced data security by allowing selective data access, benefiting analysis while maintaining security.
NEW QUESTION # 68
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