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

Section	Weight	Objectives
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Monitoring, Logging, and Troubleshooting	~8%	- Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures
Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs
CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Data Transformation, Cleansing, and Quality	~12%	- Enforce data quality and quarantine bad data - Apply advanced Spark transformations
Developing Code for Data Processing using Python and SQL	~22%	- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures
Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering

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Databricks Certified Data Engineer Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. 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?

A. Can manage
B. Can run
C. Can Read
D. Can edit

Answer: C

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.

NEW QUESTION # 170
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.

Answer: A

NEW QUESTION # 171
The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. On Heap Memory Usage is within 75% of off Heap Memory usage
B. Size on Disk is> 0
C. The RDD Block Name included the '' annotation signaling failure to cache
D. The number of Cached Partitions> the number of Spark Partitions
E. Size on Disk is < Size in Memory

Answer: B

Explanation:
When using Spark's MEMORY_ONLY storage level, the ideal scenario is that the data is fully cached in memory, and the Size on Disk should be 0 (indicating that the data is not spilled to disk). If the Size on Disk is greater than 0, it suggests that some data has been spilled to disk, which can lead to degraded performance as reading from disk is slower than reading from memory.

NEW QUESTION # 172
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. The table name change is recorded in the Delta transaction log.
B. A new Delta transaction log Is created for the renamed table.
C. The table reference in the metastore is updated and all data files are moved.
D. All related files and metadata are dropped and recreated in a single ACID transaction.
E. The table reference in the metastore is updated and no data is changed.

Answer: E

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

NEW QUESTION # 173
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
B. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Answer: D

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.

NEW QUESTION # 174
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