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| Section | Objectives |
|---|---|
| Topic 1: Production Pipelines and Orchestration | - Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance |
| Topic 2: Security, Governance, Monitoring, and Optimization | - Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control |
| Topic 3: Data Ingestion and Transformation | - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks |
| Topic 4: Data Modeling and Storage | - Schema evolution and data partitioning strategies - Delta Lake table design and optimization - Design scalable data lakehouse architectures |
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NEW QUESTION # 73
Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?
Answer: C
NEW QUESTION # 74
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?
Answer: C
Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table.
While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.References:
* Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel
* Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake
NEW QUESTION # 75
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Which step must also be completed to put the proposed query into production?
Answer: C
Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured- streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured- streaming/production.html#checkpointing
NEW QUESTION # 76
Which of the following is not a privilege in the Unity catalog?
Answer: C
Explanation:
Explanation
The Answer is DELETE and UPDATE permissions do not exit, you have to use MODIFY which provides both Update and Delete permissions.
Please note: TABLE ACL privilege types are different from Unity Catalog privilege types, please read the question carefully.
Here is the list of all privileges in Unity Catalog:
Unity Catalog Privileges
https://learn.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/language-manual/sql-ref-privileges#priv Table ACL privileges
https://learn.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#privileges
NEW QUESTION # 77
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
Answer: B
Explanation:
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.
NEW QUESTION # 78
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