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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Topic 2: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Topic 4: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 5: Data Modelling | 6% | - Delta Lake table design - Medallion Architecture implementation - Schema design and management |
| Topic 6: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Topic 7: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Topic 8: Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Topic 9: Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Topic 10: 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 |
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NEW QUESTION # 207
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Events are recorded once per minute per device.
df has the following schema: device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT Code block:
df.withWatermark( " event_time " , " 10 minutes " )
.groupBy(
________,
" device_id "
)
.agg(
avg( " temp " ).alias( " avg_temp " ),
avg( " humidity " ).alias( " avg_humidity " )
)
.writeStream
.format( " delta " )
.saveAsTable( " sensor_avg " )
Which line of code correctly fills in the blank within the code block to complete this task?
Answer: B
Explanation:
* Exact extract: "window(timeColumn, windowDuration[, slideDuration]) returns a time window for grouping on event-time."
* Exact extract: "If slideDuration is not specified the windows are non-overlapping (tumbling) with length windowDuration." References: Structured Streaming window aggregations; Watermarking.
NEW QUESTION # 208
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?
Answer: A
Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO target_table_name [target_alias]
2. USING source_table_reference [source_alias]
3. ON merge_condition
4. [ WHEN MATCHED [ AND condition ] THEN matched_action ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN not_matched_action ] [...]
6.
7.matched_action
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.not_matched_action
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])
NEW QUESTION # 209
Which of the following is true of Delta Lake and the Lakehouse?
Answer: A
Explanation:
Explanation
This is the correct answer because it is true of Delta Lake and the Lakehouse. Delta Lake uses Parquet as the underlying storage format for data files. Parquet is a columnar format that compresses data by column rather than by row. This means that Parquet can achieve high compression ratios for columns that have low cardinality or high repetition of values, such as integers, booleans, or dates. However, for columns that have high cardinality or low repetition of values, such as strings, Parquet cannot compress data very well.
Therefore, strings will only be compressed when a character is repeated multiple times within a row. Verified References:[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features - Schema enforcement and evolution" section.
NEW QUESTION # 210
The business intelligence team has a dashboard configured to track various summary metrics for retail stories.
This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:
For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:
Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?
Answer: D
NEW QUESTION # 211
A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?
Answer: A
NEW QUESTION # 212
......
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