Databricks Certified-Data-Engineer-Professional Exam | Certified-Data-Engineer-Professionalテスト模擬問題集 -要求のために常に利用可能

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

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

>> Certified-Data-Engineer-Professionalテスト模擬問題集 <<

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Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題 (Q94-Q99):

質問 # 94
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. All records are cached to an operational database and then the filter is applied
B. All records are cached to attached storage and then the filter is applied
C. The Hive metastore is scanned for min and max statistics for the latitude column
D. The Delta log is scanned for min and max statistics for the latitude column
E. The Parquet file footers are scanned for min and max statistics for the latitude column

正解：D

解説：
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.

質問 # 95
Which statement regarding stream-static joins and static Delta tables is correct?

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

正解：B

解説：
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.

質問 # 96
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

正解：E

解説：
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

質問 # 97
Which Python variable contains a list of directories to be searched when trying to locate required modules?

A. sys.path
B. importlib.resource path
C. pypi.path
D. os.path
E. pylib.source

正解：A

解説：
sys. path is a built-in variable within the sys module. It contains a list of directories that the interpreter will search in for the required module.

質問 # 98
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Create a new table for the analytics team using a CTAS statement.
B. Shallow clone the table for the analytics team.
C. Deep clone the table for the analytics team.
D. Give the analytics team direct access to the production table.

正解：B

解説：
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

質問 # 99
......

現実はしばしば残酷です。私たちは他の人と競争するために何をしますか？ Databricks証明書など、より便利な証明書ですか？おそらく、手元にあるいくつかの資格が最大の資産であり、Certified-Data-Engineer-Professional試験準備はCertified-Data-Engineer-Professional試験に迅速に合格し、すぐに認定を取得することでその資金を提供することです。それについて疑ってはいけません。より有用な認定は、より多くの方法を意味します。 Certified-Data-Engineer-Professional試験に合格すると、Certified-Data-Engineer-Professional試験の急流に関連するビジネスを持つすべての企業に歓迎されます。

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