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Valid Databricks-Certified-Professional-Data-Engineer Test Cram - Test Databricks-Certified-Professional-Data-Engineer Dumps

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Databricks Certified Professional Data Engineer is a certification exam that measures individuals' knowledge and skills in using Databricks to manipulate big data. Databricks is a cloud-based data processing platform that allows data engineers to build, deploy, and manage big data processing pipelines. Databricks Certified Professional Data Engineer Exam certification exam is designed to validate the expertise of data engineers who work with Databricks.

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Quiz 2025 Databricks-Certified-Professional-Data-Engineer: High-quality Valid Databricks Certified Professional Data Engineer Exam Test Cram

Our Databricks-Certified-Professional-Data-Engineer study practice guide takes full account of the needs of the real exam and conveniences for the clients. Our Databricks-Certified-Professional-Data-Engineer certification questions are close to the real exam and the questions and answers of the test bank cover the entire syllabus of the real exam and all the important information about the exam. Our Databricks-Certified-Professional-Data-Engineer Learning Materials can stimulate the real exam's environment to make the learners be personally on the scene and help the learners adjust the speed when they attend the real Databricks-Certified-Professional-Data-Engineer exam.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
What is the purpose of a silver layer in Multi hop architecture?

A. Replaces a traditional data lake
B. A schema is enforced, with data quality checks.
C. Optimized query performance for business-critical data
D. Efficient storage and querying of full and unprocessed history of data
E. Refined views with aggregated data

Answer: B

Explanation:
Explanation
The answer is, A schema is enforced, with data quality checks.
Medallion Architecture - Databricks
Silver Layer:
1.Reduces data storage complexity, latency, and redundency
2.Optimizes ETL throughput and analytic query performance
3.Preserves grain of original data (without aggregation)
4.Eliminates duplicate records
5.production schema enforced
6.Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.

NEW QUESTION # 120
Review the following error traceback:

Which statement describes the error being raised?

A. There is a type error because a column object cannot be multiplied.
B. There is a syntax error because the heartrate column is not correctly identified as a column.
C. There is no column in the table named heartrateheartrateheartrate
D. There is a type error because a DataFrame object cannot be multiplied.
E. The code executed was PvSoark but was executed in a Scala notebook.

Answer: C

Explanation:
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns 'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as 'heartrate'. Reference: AnalysisException

NEW QUESTION # 121
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 overall size of data in the table
B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
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 based on the amount of data in each partition

Answer: B

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 # 122
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?

A. The Hive metastore is scanned for min and max statistics for the latitude column
B. The Parquet file footers are scanned for min and max statistics for the latitude column
C. The Delta log is scanned for min and max statistics for the latitude column
D. All records are cached to an operational database and then the filter is applied
E. All records are cached to attached storage and then the filter is applied

Answer: C

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 # 123
What type of table is created when you create delta table with below command?
CREATE TABLE transactions USING DELTA LOCATION "DBFS:/mnt/bronze/transactions"

A. Delta Lake table
B. Managed delta table
C. Temp table
D. Managed table
E. External table

Answer: E

Explanation:
Explanation
Anytime a table is created using the LOCATION keyword it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/" format -> DELTA, JSON, CSV, PARQUET, TEXT I created the table command based on the above question, you can see it created an external table,

NEW QUESTION # 124
......

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