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| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Data Modelling | 6% | - Medallion Architecture implementation - Schema design and management - Delta Lake table design |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Data encryption and masking - Access control and permissions |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
| Data Ingestion & Acquisition | 7% | - Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution |
| Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices |
| Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
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NEW QUESTION # 83
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:
A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?
Answer: C
Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints: https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions
NEW QUESTION # 84
What statement is true regarding the retention of job run history?
Answer: E
Explanation:
Explanation
This is the correct answer because it is true regarding the retention of job run history. Job run history is the information about each run of a job, such as the start time, end time, status, logs, and output. Job run history is retained for 30 days by default, during which time you can view it in the Jobs UI or access it through the Jobs API. You can also deliver job run logs to DBFS or S3 using the Log Delivery feature, which allows you to specify a destination path and a delivery frequency for each job. By delivering job run logs to DBFS or S3, you can preserve them beyond the 30-day retention period and use them for further analysis or troubleshooting. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section;Databricks Documentation, under "Job run history" section; Databricks Documentation, under
"Log Delivery" section.
NEW QUESTION # 85
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling.
How should the data engineer address this issue?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
When Spark performs wide transformations such as sortBy or orderBy, large data volumes can exceed memory limits, causing disk spilling. The official Databricks performance tuning guide recommends increasing the shuffle partition count to distribute the data more evenly across executors. By default, Spark uses a fixed number of shuffle partitions (e.g., 200), which can lead to memory imbalance and spill if some partitions are too large. Increasing this number (via spark.sql.shuffle.partitions) results in smaller partitions, reduced in-memory pressure, and improved sort performance. Other options like broadcast joins or single partition sorts do not apply to single-table sorts, and converting to filters changes query logic. Thus, option D is the correct remedy.
NEW QUESTION # 86
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?
Answer: C
Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.
Reference:
Databricks Documentation on Delta Live Tables: Delta Live Tables Guide
NEW QUESTION # 87
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "When using strings directly, Spark SQL interprets them as literal column names." Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate". The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.
NEW QUESTION # 88
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