Databricks-Certified-Data-Engineer-Professional인기덤프문제 & Databricks-Certified-Data-Engineer-Professional시험대비최신덤프문제

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

Section	Weight	Objectives
Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices
Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture
Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit
Data Transformation, Cleansing, and Quality	10%	- Enforce data quality standards - Implement schema evolution and management - Apply data cleansing and validation rules
Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code
Data Governance	7%	- Manage data assets and metadata - Enforce data policies and standards - Use Unity Catalog for governance
Monitoring and Alerting	10%	- Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics
Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Data Sharing and Federation	5%	- Implement Lakehouse Federation - Use Delta Sharing for secure data sharing - Manage cross-platform data access

>> Databricks-Certified-Data-Engineer-Professional인기덤프문제 <<

Databricks-Certified-Data-Engineer-Professional인기덤프문제 시험대비 덤프공부

Databricks Databricks-Certified-Data-Engineer-Professional인증시험은 전문적인 관련지식을 테스트하는 인증시험입니다. Fast2test는 여러분이Databricks Databricks-Certified-Data-Engineer-Professional인증시험을 통과할 수 잇도록 도와주는 사이트입니다. 여러분은 응시 전 저희의 문제와 답만 잘 장악한다면 빠른 시일 내에 많은 성과 가 있을 것입니다.

최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q88-Q93):

질문 # 88
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. streamingDF.join(userLookup, ["user_id"], how="outer")
B. streamingDF.join(userLookup, ["userid"], how="inner")
C. userLookup.join(streamingDF, ["user_id"], how="right")
D. userLookup.join(streamingDF, ["userid"], how="inner")
E. streamingDF.join(userLookup, ["user_id"], how="left")

정답：A

설명：
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

질문 # 89
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
B. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
C. Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
D. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
E. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.

정답：A

질문 # 90
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
B. Iterating through a stored secret and printing each character will display secret contents in plain text.
C. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
D. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
E. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.

정답：A

설명：
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets.

질문 # 91
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
B. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
C. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
D. Delete the failed run, disable the cluster policy, and re-execute all tasks.

정답：C

설명：
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

질문 # 92
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
D. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
E. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

정답：C

설명：
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

질문 # 93
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