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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Data Modelling | 6% | - Delta Lake table design - Medallion Architecture implementation - Schema design and management |
| Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
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NEW QUESTION # 170
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables.
Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?
Answer: C
Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.
NEW QUESTION # 171
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?
Answer: E
Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.
NEW QUESTION # 172
A data engineer is developing a notebook that will run on serverless compute. The notebook requires a custom Python library distributed as a wheel file (mylib-1.0-py3-none-any.whl). The engineer has uploaded the archive to a Unity Catalog volume at /Volumes/main/shared/libs/mylib-1.0-py3-none-any.whl and now needs to install it in the notebook.
What should the data engineer do to install the library?
Answer: C
Explanation:
Databricks supports installing Python wheel packages directly from Unity Catalog Volumes.
The %pip install command accepts an absolute volume path, allowing a notebook to install the specific wheel without requiring a public or private package registry.
This approach is particularly appropriate when packages are stored in governed locations and external network access is restricted.
Unity Catalog provides permission controls over the volume, allowing administrators to manage access to the package.
Option B introduces an unnecessary external package registry. Option C relies on DBFS storage, which Databricks discourages for managing shared libraries. Option D introduces an unnecessary package-resolution mechanism when the exact wheel location is already known.
Serverless notebooks also support declaring the wheel as a dependency in the Environment panel for reproducible execution.
NEW QUESTION # 173
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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.
NEW QUESTION # 174
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?
Answer: A
Explanation:
Explanation
The answer is Data Plane, which is where compute(all-purpose, Job Cluster, DLT) are stored this is generally a customer cloud account, there is one exception SQL Warehouses, currently there are 3 types of SQL Warehouse compute available(classic, pro, serverless), in classic and pro compute is located in customer cloud account but serverless computed is located in Databricks cloud account.
Diagram, timeline Description automatically generated
NEW QUESTION # 175
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