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[General] 100% Pass Databricks - Databricks-Certified-Professional-Data-Engineer–High Pass

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【General】 100% Pass Databricks - Databricks-Certified-Professional-Data-Engineer–High Pass

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It is common in modern society that many people who are more knowledgeable and capable than others finally lost some good opportunities for development because they didn’t obtain the Databricks-Certified-Professional-Data-Engineer certification. The prerequisite for obtaining the Databricks-Certified-Professional-Data-Engineer Certification is to pass the exam, but not everyone has the ability to pass it at one time. But our Databricks-Certified-Professional-Data-Engineer exam questions will help you pass the exam by just one go for we have the pass rate high as 98% to 100%.
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
TopicDetails
Topic 1
  • Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 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.

Achieving the Databricks Certified Professional Data Engineer certification is a valuable asset for data professionals. It demonstrates that the individual has the necessary knowledge and skills to work with big data and cloud computing technologies, specifically with Databricks Unified Analytics Platform. Databricks Certified Professional Data Engineer Exam certification is recognized by leading organizations and can help individuals advance their careers in the field of data engineering. It also provides a competitive advantage over other candidates while applying for jobs in the field of big data and cloud computing.
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Databricks Certified Professional Data Engineer exam is a vendor-neutral certification, meaning it is not specific to any particular technology or product. This makes it an excellent choice for data engineers who work with different big data technologies and want to demonstrate their knowledge of Databricks. Databricks Certified Professional Data Engineer Exam certification exam is recognized globally, and it is highly valued by organizations that use Databricks for their big data processing needs.
Databricks Certified Professional Data Engineer Exam Sample Questions (Q85-Q90):NEW QUESTION # 85
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?
  • A. The badRecordsPath location is accumulating many small files.
  • B. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
  • C. The source data is valid JSON but does not conform to the defined schema in some way.
  • D. At some point, the upstream data provider switched everything to multi-line JSON.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Auto Loader quarantines records that fail schema validation, even if they are syntactically valid JSON. The documentation explains that "records that do not match the defined schema are considered corrupt and written to the bad records path." In this case, the data likely contains valid JSON fields whose structure or data types differ from the declared schema (a int, b int). For example, if a field expected as an integer arrives as a string, Auto Loader will quarantine it. The presence of badRecordsPath confirms this behavior. The cloudFiles.quarantineMode option controls quarantine behavior but is unrelated to valid data that fails schema validation. Thus, the correct cause is C.

NEW QUESTION # 86
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 are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
  • B. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
  • C. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
  • D. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
  • E. 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.
Answer: C
Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:
* Grant privileges on a database:
https://docs.databricks.com/en/s ... leges-database.html
* Privileges you can grant on Hive metastore objects:
https://docs.databricks.com/en/s ... cls/privileges.html

NEW QUESTION # 87
Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?
  • A. Use Incremental refresh to run at 8 AM every day.
  • B. The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.
  • C. Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard
  • D. A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.
  • E. Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.
Answer: B
Explanation:
Explanation
The answer is,
The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at
8 AM.
Automatically refresh a dashboard
A dashboard's owner and users with the Can Edit permission can configure a dashboard to auto-matically refresh on a schedule. To automatically refresh a dashboard:
* Click the Schedule button at the top right of the dashboard. The scheduling dialog appears.
* Graphical user interface, text, application, email, Teams Description automatically generated
* 2.In the Refresh every drop-down, select a period.
* 3.In the SQL Warehouse drop-down, optionally select a SQL warehouse to use for all the queries.
If you don't select a warehouse, the queries execute on the last used SQL ware-house.
* 4.Next to Subscribers, optionally enter a list of email addresses to notify when the dashboard is automatically updated.
* Each email address you enter must be associated with a Azure Databricks account or con-figured as an alert destination.
* 5.Click Save. The Schedule button label changes to Scheduled.

NEW QUESTION # 88
You are currently working on storing data you received from different customer surveys, this data is highly unstructured and changes over time, why Lakehouse is a better choice compared to a Data warehouse?
  • A. Lakehouse supports ACID
  • B. Lakehouse supports schema enforcement and evolution, traditional data warehouses lack schema evolution.
  • C. Lakehouse supports primary and foreign keys like a data warehouse
  • D. Lakehouse supports SQL
  • E. Lakehouse enforces data integrity
Answer: B

NEW QUESTION # 89
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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: D
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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CREATE VIEW" section.

NEW QUESTION # 90
......
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