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| Section | Weight | Objectives |
|---|---|---|
| Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance |
| Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines |
| Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs |
| Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
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NEW QUESTION # 48
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?
Answer: A
NEW QUESTION # 49
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?
Answer: A
Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.
NEW QUESTION # 50
Which of the following is true of Delta Lake and the Lakehouse?
Answer: C
Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.
NEW QUESTION # 51
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?
Answer: A
Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.
NEW QUESTION # 52
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?
Answer: D
Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.
NEW QUESTION # 53
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