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| Section | Weight | Objectives |
|---|---|---|
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| 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 Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
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NEW QUESTION # 35
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?
Answer: C
Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.
NEW QUESTION # 36
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?
Answer: B
Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.
NEW QUESTION # 37
How do stored procedures contribute to the efficiency of data analysis using SQL?
Answer: C
Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.
NEW QUESTION # 38
When automating data processing, what significance do logging and monitoring solutions hold in ensuring seamless operations?
Answer: A
Explanation:
Logging and monitoring solutions help identify processing bottlenecks, ensuring seamless operations in automated data processing.
NEW QUESTION # 39
You are building a dashboard to monitor the performance of a Snowflake data pipeline. This pipeline ingests data from various sources, transforms it, and loads it into target tables. You want to visualize the overall pipeline latency, including the time spent in each stage (ingestion, transformation, loading). You have access to event logs that capture the start and end timestamps for each stage of each pipeline run. The logs are stored in a Snowflake table named 'PIPELINE LOGS' with columns: 'PIPELINE RUN (VARCHAR), 'STAGE_NAME' (VARCHAR), 'START_TIMESTAMP' (TIMESTAMP_NU), 'END_TIMESTAMP (TIMESTAMP_NTZ). Which visualization type and query construct provides the MOST effective way to visualize the latency of each stage within each pipeline run, allowing for easy identification of bottlenecks?
Answer: E
Explanation:
A Gantt chart (C) is the most effective visualization for this scenario. It directly shows the start and end times of each stage within each pipeline run, making it easy to visually identify bottlenecks and understand the overall timeline. The query would need to calculate the duration of each stage using 'TIMESTAMPDIFF()' to determine the length of each bar in the Gantt chart. The other options provide aggregated summaries (A, D) or distributions (B) that don't directly show the temporal relationship between stages within each pipeline run. Heatmap is not so useful here.
NEW QUESTION # 40
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