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| Section | Objectives |
|---|---|
| Topic 1: Security and Data Governance | - Data masking and encryption - Role-based access control (RBAC) - Secure data sharing |
| Topic 2: Data Engineering Fundamentals | - Data pipelines concepts and patterns - Snowflake architecture for data engineering |
| Topic 3: Data Ingestion and Integration | - Staging data and loading mechanisms - Batch and streaming ingestion approaches - Snowpipe usage and automation |
| Topic 4: Performance and Optimization | - Clustering and partition strategies - Query optimization techniques - Warehouse sizing and scaling |
| Topic 5: Data Transformation and Processing | - SQL-based transformations in Snowflake - Handling semi-structured data (JSON, Avro, Parquet) - Streams and Tasks for ELT pipelines |
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NEW QUESTION # 220
A Snowflake table 'PRODUCT REVIEWS' is being ingested into from an external system. You have a stream 'PRODUCT REVIEWS STREAM' defined on this table to capture changes. Due to a bug in the ingestion process, incorrect data was loaded for a specific period. You need to correct the data'. Which of the following SQL statements, when executed against the 'PRODUCT REVIEWS STREAM' , will return the number of rows that were inserted, updated, and deleted during that period?
Answer: D
Explanation:
Option D is correct: The query 'SELECT METADATA$ACTION, count( ) FROM GROUP BY METADATA$ACTION$ will correctly return the number of rows for each action (INSERT, UPDATE, DELETE) within the stream. Options A, B, and C will give a total of only a single action type and E would give the total number of records in the stream regardless of action, failing to provide granular count per action.
NEW QUESTION # 221
You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?
Answer: E
Explanation:
Option C is the most effective solution. 'ON_ERROR = CONTINUE allows the 'COPY INTO' command to proceed even if it encounters errors (e.g., due to extra columns). = CASE INSENSITIVE' handles slight variations in column names between the CSV and the Snowflake table. Setting = TRUE in the Snowflake session (this is a hypothetical feature, Snowflake doesn't directly have AUTO_CREATE_TABLE in COPY INTO, so this tests knowledge of workarounds and understanding of COPY INTO capabilities). Other options have drawbacks. Option A doesn't directly use COPY INTO and is generally slower. Option B requires a predefined schema, negating the ability to handle schema evolution. Option D is used with in memory stream, rather s3. Option E involves manual schema inspection and construction, which is cumbersome and error-prone.
NEW QUESTION # 222
A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?
Answer: A
Explanation:
'JSON ARRAYAGG' is the most efficient way to construct a JSON array from a result set in Snowflake. returns a Snowflake array, which is different from a JSON array. 'TO_JSON' is used to convert a Snowflake VARIANT to a JSON string, and 'ARRAY CONSTRUCT creates a Snowflake array from individual elements. Option E is close but creates an object with single key products and the value being the Array. 'JSON_ARRAYAGG' directly builds a JSON Array.
NEW QUESTION # 223
You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?
Answer: A
Explanation:
Option D is the most likely reason. Snowpipe relies on cloud storage event notifications (e.g., SQS for AWS S3, Event Grid for Azure Blob Storage, Pub/Sub for Google Cloud Storage) to trigger data ingestion. If these notifications are not properly configured, Snowpipe will not be notified of new files being added to the storage location, resulting in skipped files. Option A is possible, but less likely if the pipe was just created and initial data loading is failing. Option B is incorrect; Snowpipe detects files based on event notifications, not by continuously scanning the storage location. Option C could be an issue, but the question states the file format is defined. Option E is incorrect; Snowpipe does support Avro format.
NEW QUESTION # 224
You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?
Answer: E
Explanation:
The 'AT (TIMESTAMP=> ...)' clause allows you to specify a specific point in time to clone a table. The 'TIMESTAMP LTZ data type accurately represents a timestamp with a time zone, which is crucial for time travel operations. The other options either use incorrect syntax, incorrect clauses C BEFORE is for statements, not specific times), or do not correctly represent the timestamp.
NEW QUESTION # 225
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