Databricks-Certified-Professional-Data-Engineer Quizfragen Und Antworten, Databricks-Certified-Professional-Data-Engineer Deutsche

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

Section	Weight	Objectives
Topic 1: Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Topic 2: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs
Topic 3: Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion
Topic 4: Data Governance	7%	- Data lineage and metadata tracking - Policy enforcement - Unity Catalog management
Topic 5: Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks
Topic 6: Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Topic 7: Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing)
Topic 8: Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications
Topic 9: Ensuring Data Security and Compliance	10%	- Compliance standards implementation - Data encryption and masking - Access control and permissions
Topic 10: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q159-Q164):

159. Frage
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Antwort: C

Begründung:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. Reference:
Constraints: https://docs.delta.io/latest/delta-constraints.html
ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

160. Frage
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

A. Ridge and Lasso
B. Apriori Algorithm
C. Integer programming
D. Ordinary Least squares

Antwort: D

Begründung:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters

161. Frage
A junior data engineer on your team has implemented the following code block.

The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

A. They are deleted.
B. They are merged.
C. They are updated.
D. They are inserted.
E. They are ignored.

Antwort: E

Begründung:
Explanation
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged."https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20o

162. Frage
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C. The connection to the external table will fail; the string "redacted" will be printed.
D. The connection to the external table will succeed; the string "redacted" will be printed.
E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Antwort: D

Begründung:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified Reference: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under "Secrets" section.

163. Frage
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?

A. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
B. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
C. Parse the Delta Lake transaction log to identify all newly written data files.
D. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

Antwort: D

Begründung:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
Reference:
Delta Lake Documentation on Time Travel: Delta Time Travel
Delta Lake Versioning: Delta Lake Versioning Guide

164. Frage
......

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