Certified-Data-Engineer-Professional認定試験トレーリング & Certified-Data-Engineer-Professional試験勉強過去問

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

Section	Objectives
Topic 1: Data Transformation, Cleansing, and Quality	- Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data - Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets
Topic 2: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure sharing with external platforms using the open sharing protocol 2. Configure Databricks-to-Databricks Sharing 3. Share live Lakehouse data with external computing platforms
Topic 3: Ensuring Data Security and Compliance	- Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies - Data Security 1. Apply anonymization and pseudonymization techniques 2. Use ACLs to secure workspace objects and enforce least privilege 3. Use row filters and column masks for sensitive data
Topic 4: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Design and implement scalable data models using Delta Lake 3. Understand Liquid Clustering versus partitioning and Z-Ordering
Topic 5: Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering
Topic 6: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 2. Manage and troubleshoot third-party library installations and dependencies 3. Develop User-Defined Functions using Pandas/Python UDFs - Building and Testing ETL Pipelines 1. Use APPLY CHANGES APIs for change data capture 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 4. Develop unit and integration tests for data processing code 5. Compare streaming tables and materialized views 6. Use control flow operators in pipeline components 7. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 8. Configure environments, dependencies, memory, and retry behavior
Topic 7: Data Governance	- Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model - Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data
Topic 8: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Build append-only pipelines for batch and streaming data using Delta 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Ingest data from message buses and cloud storage
Topic 9: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Topic 10: Monitoring and Alerting	- Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 3. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 4. Use Query Profiler and Spark UI to monitor workloads - Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues

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Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題 (Q139-Q144):

質問 # 139
Which statement describes the correct use of pyspark.sql.functions.broadcast?

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

正解：B

解説：
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.

質問 # 140
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
B. All records from all columns will be displayed with the values in user_ltv.
C. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
D. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
E. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

正解：C

解説：
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

質問 # 141
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Define a view against the products_per_order table and define the dashboard against this view.
B. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
C. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
D. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
E. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.

正解：E

質問 # 142
Which Python variable contains a list of directories to be searched when trying to locate required modules?

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

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 # 143
A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
B. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.
C. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.
D. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.

正解：D

解説：
Separating the quarantine processing from the low-latency production stream ensures the good- data pipeline cannot be slowed down or destabilized by quarantine writes or failures. Filtering out negative values in the production stream preserves latency and reliability for downstream systems. Running the quarantine stream on a small, independent cluster on a daily schedule minimizes cost while still capturing the bad records for periodic analysis, and it fully isolates quarantine operations from production dependencies.

質問 # 144
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