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Databricks Certified Professional Data Engineer certification exam is a challenging exam that requires candidates to have a deep understanding of Databricks technologies and data engineering concepts. Candidates must have experience working with Apache Spark, Delta Lake, SQL, and Python. They must also have experience working with cloud-based data platforms such as AWS, Azure, or Google Cloud Platform.

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The Databricks Certified Professional Data Engineer Exam certification exam covers a broad range of topics, including data processing with Spark, data engineering with Delta Lake, machine learning with MLflow, and cloud infrastructure with AWS and Azure. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's understanding of these topics and their ability to apply this knowledge to real-world scenarios. Databricks-Certified-Professional-Data-Engineer Exam is also designed to test the candidate's ability to design and implement scalable data pipelines and machine learning workflows using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q189-Q194):

NEW QUESTION # 189
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

A. "{region}+{store}+_sales_+{year}"
B. f"{region}{store}_sales_{year}"
C. "{region}+{store}+"_sales_"+{year}"
D. f"{region}+{store}+_sales_+{year}"
E. "{region}{store}_sales_{year}"

Answer: B

NEW QUESTION # 190
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
C. Both commands will fail. No new variables, tables, or views will be created.
D. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Answer: E

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 191
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format("parquet")
.load("/mnt/raw_orders/")
.withWatermark("time", "2 hours")
.dropDuplicates(["customer_id", "order_id"])
.writeStream
.trigger(once=True)
.table("orders")
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
B. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
C. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
D. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay."
* Exact extract: "Records older than the event-time watermark are considered late and may be dropped." References: Structured Streaming watermarking and deduplication; One-time trigger.

NEW QUESTION # 192
The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

A. The globally unique ID of the newly triggered run.
B. The job_id and number of times the job has been run are concatenated and returned.
C. The number of times the job definition has been run in this workspace.
D. The job_id is returned in this field.

Answer: A

Explanation:
* Exact extract: "run_id: The canonical identifier of a run."
References: Databricks Jobs API/CLI response fields.

NEW QUESTION # 193
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Answer: D

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section. https://docs.databricks.com/en/delta/tune-file-size.
html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 194
......

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