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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Objectives
Topic 1: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)
Topic 2: Delta Lake and Data Management	- Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning
Topic 3: Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals
Topic 4: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 5: Production Pipelines and Orchestration	- Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. The badRecordsPath location is accumulating many small files.
B. At some point, the upstream data provider switched everything to multi-line JSON.
C. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
D. The source data is valid JSON but does not conform to the defined schema in some way.

Answer: D

Explanation:
Auto Loader quarantines records that cannot be parsed according to the specified schema, even if the JSON itself is well formed. If incoming records contain additional fields, missing fields, or incompatible data types compared to the declared schema, they are treated as invalid and routed to the bad records path.

NEW QUESTION # 74
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?

A. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
B. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
C. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
D. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours

Answer: B

NEW QUESTION # 75
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
B. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster
C. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
D. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Answer: B

Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.

NEW QUESTION # 76
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Use ZORDER on high-cardinality columns.
B. Enable deletion vectors on the table if not already enabled.
C. Overwrite the table instead of Merge.
D. Partition the table by date.
E. Apply liquid clustering using the merge join keys.

Answer: B,E

Explanation:
Liquid clustering on the merge join keys improves data locality and reduces the amount of data scanned during MERGE operations, which is especially effective for large, frequently updated tables. Enabling deletion vectors avoids rewriting entire Parquet files for updates and deletes, significantly reducing I/O and improving MERGE performance on Unity Catalog-managed tables.

NEW QUESTION # 77
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track state information for the unique keys present in the join.
B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
D. Stream-static joins cannot use static Delta tables because of consistency issues.
E. The checkpoint directory will be used to track updates to the static Delta table.

Answer: B

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

NEW QUESTION # 78
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