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| Section | Weight | Objectives |
|---|---|---|
| Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs |
| Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
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NEW QUESTION # 182
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: B
Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset
NEW QUESTION # 183
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.
NEW QUESTION # 184
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?
Answer: B
Explanation:
Salting distributes records for heavily skewed keys across multiple partitions by adding a random prefix, which balances task execution during the aggregation. A second aggregation after removing the prefix correctly recombines the partial results, eliminating skew-related bottlenecks without losing accuracy.
NEW QUESTION # 185
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")
The data engineer notices the query slowing down significantly.
Which solution would improve the performance?
Answer: C
NEW QUESTION # 186
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?
Answer: C
Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage, data skipping, and file pruning. According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters, a process called data skipping and file pruning. Additionally, Delta uses a vectorized Parquet reader, which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.
NEW QUESTION # 187
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