Databricks Databricks-Certified-Professional-Data-Engineer Exam Cram Review | Databricks-Certified-Professional-Data-Engineer Valid Test Papers

Our Databricks-Certified-Professional-Data-Engineer questions answers study guide is the best option for you to pass exam easily. Our experts are busy in providing the most updated content that could ensure your 100% success in Databricks-Certified-Professional-Data-Engineer actual test. The up-to-date Databricks exam dumps consist of latest practice questions answers and explanations. We are devoted to take appropriate steps in improving our products like Databricks-Certified-Professional-Data-Engineer Pass Guide.

We truly treat our customers with the best quality service and the most comprehensive Databricks-Certified-Professional-Data-Engineer exam pdf, that's why we enjoy great popularity among most IT workers. When you want to learn something about the Databricks-Certified-Professional-Data-Engineer Online Training, our customer assisting will be available for you. We will offer you the best preparation materials regarding Databricks-Certified-Professional-Data-Engineer practice exam. You can totally trust our dumps and service.

>> Databricks Databricks-Certified-Professional-Data-Engineer Exam Cram Review <<

Databricks Databricks-Certified-Professional-Data-Engineer Pre-Exam Practice Tests | ITexamReview

Our Databricks-Certified-Professional-Data-Engineer practice quiz will be the optimum resource. Many customers claimed that our study materials made them at once enlightened after using them for review. If you are still tentative about our Databricks-Certified-Professional-Data-Engineer exam dumps, and some exam candidate remain ambivalent to the decision of whether to choose our Databricks-Certified-Professional-Data-Engineer Training Materials, there are free demos for your reference for we understand your hesitation.

Databricks Certified Professional Data Engineer certification exam is an important credential for data engineers who work with Databricks. Databricks-Certified-Professional-Data-Engineer Exam Tests the candidate's ability to design, build, and maintain data pipelines, as well as their knowledge of various data engineering tools and techniques. Databricks Certified Professional Data Engineer Exam certification can enhance the holder's career prospects and demonstrate their proficiency in using Databricks for data engineering tasks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

Answer: A

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 171
Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

A. Setup multiple clusters so each team member has their own cluster
B. Disable the auto-scaling feature
C. Use High concurrency mode instead of the standard mode
D. Increase the size of the driver node

Answer: C

Explanation:
Explanation
The answer is Use High concurrency mode instead of the standard mode,
https://docs.databricks.com/clusters/cluster-config-best-practices.html#cluster-mode High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs.
Databricks recommends enabling autoscaling for High Concurrency clusters.

NEW QUESTION # 172
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Validates a complete use case of your application
B. Yields faster deployment and execution times
C. Improves the quality of your data
D. Troubleshooting is easier since all steps are isolated and tested individually
E. Ensures that all steps interact correctly to achieve the desired end result

Answer: C

NEW QUESTION # 173
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The filter condition involves a data type excluded from data skipping support.
B. The filter is executed only after the full data scan, preventing data skipping.
C. The Delta table lacks optimization that enables dynamic file pruning.
D. The event_date column is outside the table's partitioning and Z-ordering scheme.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Delta Lake's data skipping and file pruning optimizations rely on metadata about columns used in partitioning or Z-ordering. If a filter column (e.g., event_date) is not included in the partition or Z-ordering keys, Spark cannot effectively prune files at query time, resulting in full table scans. The Databricks optimization guide states that "File pruning and data skipping are most effective when queries filter on partition or Z-order columns." This explains why the filter was applied but had no impact on the amount of data read. Options A and B are incorrect because Delta automatically applies file pruning when possible; D is less likely, as date columns are fully supported for skipping.

NEW QUESTION # 174
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, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
B. 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.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. 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.
E. 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.

Answer: D

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 # 175
......

In this fast-changing world, the requirements for jobs and talents are higher, and if people want to find a job with high salary they must boost varied skills which not only include the good health but also the working abilities. The Databricks-Certified-Professional-Data-Engineer exam torrent is compiled by the experienced professionals and of great value. You can master them fast and easily. We provide varied versions for you to choose and you can find the most suitable version of Databricks-Certified-Professional-Data-Engineer Exam Materials. So it is convenient for the learners to master the Databricks Certification questions torrent and pass the exam in a short time.

Databricks-Certified-Professional-Data-Engineer Valid Test Papers: https://www.itexamreview.com/Databricks-Certified-Professional-Data-Engineer-exam-dumps.html

Ray Cook Ray Cook

Biography

Databricks Databricks-Certified-Professional-Data-Engineer Exam Cram Review | Databricks-Certified-Professional-Data-Engineer Valid Test Papers

Databricks Databricks-Certified-Professional-Data-Engineer Pre-Exam Practice Tests | ITexamReview

Databricks Certified Professional Data Engineer Exam Sample Questions (Q170-Q175):

Archives