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Databricks-Certified-Professional-Data-Engineer Exam Dumps

Databricks-Certified-Professional-Data-Engineer Exam Dumps

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Description

Exam Name: Databricks Certified Data Engineer Professional
Exam Code: Databricks-Certified-Professional-Data-Engineer
Related Certification(s): Databricks Data Engineer Professional Certification
Certification Provider: Databricks
Number of Databricks-Certified-Professional-Data-Engineer practice questions in our database: 
Expected Databricks-Certified-Professional-Data-Engineer Exam Topics, as suggested by Databricks :

  • Module 1: Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.
  • Module 2: Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture, and addressing performance issues related to small files.
  • Module 3: Data Modeling: It focuses on understanding the objectives of data transformations, using Change Data Feed, applying Delta Lake cloning, designing multiplex bronze tables. Lastly it discusses implementing incremental processing and data quality enforcement, implementing lookup tables, and implementing Slowly Changing Dimension tables, and implementing SCD Type 0, 1, and 2 tables.
  • Module 4: Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
  • Module 5: Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
  • Module 6: Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.

Description

Exam Name: Databricks Certified Data Engineer Professional
Exam Code: Databricks-Certified-Professional-Data-Engineer
Related Certification(s): Databricks Data Engineer Professional Certification
Certification Provider: Databricks
Number of Databricks-Certified-Professional-Data-Engineer practice questions in our database: 
Expected Databricks-Certified-Professional-Data-Engineer Exam Topics, as suggested by Databricks :

  • Module 1: Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.
  • Module 2: Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture, and addressing performance issues related to small files.
  • Module 3: Data Modeling: It focuses on understanding the objectives of data transformations, using Change Data Feed, applying Delta Lake cloning, designing multiplex bronze tables. Lastly it discusses implementing incremental processing and data quality enforcement, implementing lookup tables, and implementing Slowly Changing Dimension tables, and implementing SCD Type 0, 1, and 2 tables.
  • Module 4: Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
  • Module 5: Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
  • Module 6: Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.

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Q1. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before. Why are the cloned tables no longer working?

A.The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

B. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

C. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

D. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

Correct Answer: C

Q2. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure. The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic. The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema. Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A.Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

C. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Correct Answer: D

Q3. A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function. Which kind of the test does the above line exemplify?

A.Integration

B. Unit

C. Manual

D. functional

Correct Answer: B

Q4. The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views. The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group. GRANT USAGE ON DATABASE prod TO eng; GRANT SELECT ON DATABASE prod TO eng; Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A.Group members have full permissions on the prod database and can also assign permissions to other users or groups.

B. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

C. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.

D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

E. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.

Correct Answer: D

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