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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Debugging and Deploying | 10% | - CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs |
| Topic 2: Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Compliance standards implementation - Access control and permissions |
| Topic 3: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 4: Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic |
| Topic 5: Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Topic 6: Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Topic 8: Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Topic 9: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Topic 10: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
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NEW QUESTION # 173
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
Answer: C
Explanation:
Explanation
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE
NEW QUESTION # 174
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create .
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
Answer: C
Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring and Logging" section; [Databricks Documentation], under "Jobs API - Create" section.
NEW QUESTION # 175
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should havefive workers,one driverof type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation:
TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
* Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).
* Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.
* Workers:--num-workers 5 sets the number of worker nodes to 5.
* Node Type:--node-type-id i3.xlarge defines the hardware configuration.
* Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
* Option A (databricks clusters create ...)
* Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.
* The correct CLI command for cluster creation is databricks compute create.
* Option B (databricks clusters add ...)
* Incorrect:databricks clusters addis not a valid CLI command.
* Option C (databricks compute add ...)
* Incorrect:databricks compute addis not a valid CLI command.
* Option D (databricks compute create ...)
* Correct:databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime
14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer isD.
References:
* Databricks CLI Documentation
NEW QUESTION # 176
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?
Answer: C
Explanation:
Explanation
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text.
Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "List secrets" section.
NEW QUESTION # 177
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
Answer: A
NEW QUESTION # 178
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