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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Topic 2: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 3: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics |
| Topic 4: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Topic 5: Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Topic 6: Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Topic 7: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Ingest data from diverse sources - Use Auto Loader and structured streaming |
| Topic 8: Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Topic 9: 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 |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance |
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NEW QUESTION # 237
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:
The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?
Answer: B
NEW QUESTION # 238
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?
Answer: A
Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.
NEW QUESTION # 239
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?
Answer: E
Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.
NEW QUESTION # 240
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?
Answer: D
Explanation:
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NEW QUESTION # 241
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?
Answer: D
Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.
NEW QUESTION # 242
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