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

Section	Objectives
Topic 1: Data Ingestion and Processing	- Structured Streaming fundamentals - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader
Topic 2: Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Topic 3: Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows
Topic 4: Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics)
Topic 5: Delta Lake and Data Management	- Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q236-Q241):

NEW QUESTION # 236
Which of the following is true of Delta Lake and the Lakehouse?

A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B. Z-order can only be applied to numeric values stored in Delta Lake tables
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

Answer: C

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 237
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

Answer: A

Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

NEW QUESTION # 238
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track state information for the unique keys present in the join.
B. Stream-static joins cannot use static Delta tables because of consistency issues.
C. The checkpoint directory will be used to track updates to the static Delta table.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.

Answer: D

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

NEW QUESTION # 239
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
B. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
C. Configure a job that executes every time new data lands in a given directory.
D. Schedule a job to execute the pipeline once an hour on a new job cluster.

Answer: D

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 240
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. It is retained for 60 days, after which logs are archived
C. It is retained for 60 days, during which you can export notebook run results to HTML
D. It is retained for 90 days or until the run-id is re-used through custom run configuration
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: C

NEW QUESTION # 241
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