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| Section | Objectives |
|---|---|
| Topic 1: Data Governance and Quality | - Data quality concepts and management - Data access control and governance - Unity Catalog basics |
| Topic 2: Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data |
| Topic 3: Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Topic 4: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Topic 5: Data Processing and Transformations | - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) |
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NEW QUESTION # 303
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?
Answer: C
Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Reference:
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 304
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
Answer: A
Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.
NEW QUESTION # 305
A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team.
What steps the data engineer needs to follow using built-in tools?
Answer: B
Explanation:
The recommended way to troubleshoot and collect detailed job logs is through the Job Run Details page in Databricks. From there, engineers can export run logs or configure automatic log delivery to a storage destination. The driver and event logs available under compute details provide stdout, stderr, and cluster lifecycle context required for root-cause analysis.
Reference Source: Databricks Jobs Monitoring and Logging Documentation - "Access driver logs and configure log delivery."
NEW QUESTION # 306
Differentiate between all-purpose clusters and jobs clusters.
A data engineering team has created a python notebook to load data from cloud storage, this job has been tested and now needs to be scheduled in production.
Which would be the best cluster to be used in this case?
Answer: C
NEW QUESTION # 307
A data engineer must deliver a trustworthy customer 360 dataset in Databricks for data scientists and BI teams. The engineer plans to join deduplicated customer records with cleaned transaction data, enforce schema and data quality checks, and create a conformed
"customer_transactions"view. Later, highly aggregated, domain-specific tables (for weekly spend and executive summaries) will be produced for dashboards.
Where should the engineer build the conformed "customer_transactions" dataset, and where should the aggregated, report-ready tables reside?
Answer: D
Explanation:
The conformed customer_transactions dataset belongs in Silver because it represents cleaned, deduplicated, and validated data that is suitable for broad analytical use. Aggregated, domain- specific, and report-ready tables are best placed in Gold, as this layer is designed for highly curated datasets optimized for BI, dashboards, and executive reporting.
NEW QUESTION # 308
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