Valid Braindumps Databricks-Certified-Professional-Data-Engineer Questions, Latest Databricks-Certified-Professional-Data-Engineer Study Notes

The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) certification helps you advance your career and even secure a pay raise. Today, the Databricks certification is an excellent choice for career growth, and to obtain it, you need to pass the Databricks-Certified-Professional-Data-Engineer exam which is a time-based exam. To prepare for the Databricks-Certified-Professional-Data-Engineer Exam successfully in a short time, it's essential to prepare with real Databricks-Certified-Professional-Data-Engineer exam questions. If you don't prepare with Databricks-Certified-Professional-Data-Engineer updated dumps, you will fail and lose time and money.

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

Section	Weight	Objectives
Topic 1: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations
Topic 2: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration
Topic 3: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Data governance fundamentals 3. Bronze, silver, gold data layers
Topic 4: Data Ingestion	15-20%	- Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration
Topic 5: Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Optimize and Z-order 3. Time travel and data versioning - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Shallow clone the table for the analytics team.
B. Deep clone the table for the analytics team.
C. Give the analytics team direct access to the production table.
D. Create a new table for the analytics team using a CTAS statement.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "A shallow clone creates a copy of the metadata that references the source data files; it is fast and inexpensive." Exact extract: "A deep clone copies the data." Exact extract: "Clones provide a point-in-time snapshot for experimentation without impacting the source." A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for 20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION # 131
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 number of times the job definition has been run in this workspace.
B. The job_id is returned in this field.
C. The job_id and number of times the job has been run are concatenated and returned.
D. The globally unique ID of the newly triggered run.

Answer: D

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

NEW QUESTION # 132
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Answer: B

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 133
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target 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.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B. 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.
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.
* Settingspark.sql.files.maxPartitionBytesto 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).
References:
* Apache Spark Documentation: Configuration - spark.sql.files.maxPartitionBytes
* Databricks Documentation on Data Sources: Databricks Data Sources Guide

NEW QUESTION # 134
Which of the following is true of Delta Lake and the Lakehouse?

A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E. Z-order can only be applied to numeric values stored in Delta Lake tables

Answer: C

Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3. However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.

NEW QUESTION # 135
......

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