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| Section | Weight | Objectives |
|---|---|---|
| Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Data Governance | 7% | - Manage data assets and metadata - Enforce data policies and standards - Use Unity Catalog for governance |
| Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources |
| Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
| Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
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NEW QUESTION # 219
The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?
Answer: C
Explanation:
When using Spark's MEMORY_ONLY storage level, the ideal scenario is that the data is fully cached in memory, and the Size on Disk should be 0 (indicating that the data is not spilled to disk). If the Size on Disk is greater than 0, it suggests that some data has been spilled to disk, which can lead to degraded performance as reading from disk is slower than reading from memory.
NEW QUESTION # 220
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?
Answer: E
Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.
NEW QUESTION # 221
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
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Which command should be removed from the notebook before scheduling it as a job?
Answer: B
Explanation:
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.
NEW QUESTION # 222
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
Answer: A
Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.
NEW QUESTION # 223
Which distribution does Databricks support for installing custom Python code packages?
Answer: A
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows
NEW QUESTION # 224
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