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The Databricks Databricks-Certified-Data-Engineer-Professional certification is a valuable credential that plays a significant role in advancing the Databricks professional's career in the tech industry. With the Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional) certification exam you can demonstrate your skills and knowledge level and get solid proof of your expertise. You can use this proof to advance your career. The Databricks Databricks-Certified-Data-Engineer-Professional Certification Exam enables you to increase job opportunities, promotes professional development, and higher salary potential, and helps you to gain a competitive edge in your job search.

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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Delta Lake - Unity Catalog - Lakehouse Architecture - Data Management
Topic 2: Data Processing	28%	- ETL Pipelines - Spark SQL - Structured Streaming - Data Transformation
Topic 3: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Topic 4: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 5: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A. Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
B. Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
C. Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
D. Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
E. Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.

Answer: C

Explanation:
High Concurrency clusters in Databricks are designed for multiple concurrent users and workloads. They provide fine-grained sharing of cluster resources and are optimized for operations such as running multiple parallel queries and updates. This would be suitable for a solution that involves many pipelines with parallel updates, especially with high volume and high velocity data.

NEW QUESTION # 184
Which method can be used to determine the total wall-clock time it took to execute a query?

A. Open the Query Profiler associated with that query and use the Aggregated task time metric.
B. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
C. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
D. In the Spark UI, take the job duration of the longest-running job associated with that query.

Answer: C

Explanation:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs.
Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

NEW QUESTION # 185
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
E. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Answer: B

Explanation:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed.

NEW QUESTION # 186
Which statement describes the default execution mode for Databricks Auto Loader?

A. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
E. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

Answer: A

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.

NEW QUESTION # 187
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from part- file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
B. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
C. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.

Answer: C

Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly. Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB. The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).

NEW QUESTION # 188
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