시험대비Professional-Data-Engineer퍼펙트최신버전덤프자료덤프공부자료

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration - Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools
Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Performing data quality and validation checks 3. Recovering from failures - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs
Storing the data (~20% of the exam)	20%	- Selecting storage systems 1. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Designing the data model 3. Mapping business requirements 4. Deciding the degree of data normalization - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage - Using a data lake 1. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls)
Ingesting and processing the data (~20% of the exam)	20%	- Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows) - Performing security considerations 1. Data encryption 2. Identity and Access Management (IAM) 3. Auditing, privacy, and compliance
Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cluster sizing and autoscaling 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cost optimization - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements - Designing data pipelines 1. AI data enrichment 2. Data acquisition and import 3. Streaming (e.g., windowing, late arriving data) 4. Batch processing 5. Integrating with new data sources 6. Processing logic

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최신 Google Cloud Certified Professional-Data-Engineer 무료샘플문제 (Q253-Q258):

질문 # 253
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Hashing
B. Salting
C. Field promotion
D. Randomization

정답：C

설명：
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotting

질문 # 254
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

A. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
B. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
C. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

정답：D

설명：
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in
the bid event that is processed first.

질문 # 255
You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

A. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage
B. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage
C. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
D. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.

정답：A

질문 # 256
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

A. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
B. Log debug information in each ParDo function, and analyze the logs at execution time.
C. Insert output sinks after each key processing step, and observe the writing throughput of each block.
D. Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.

정답：A

질문 # 257
You work for a large bank that operates in locations throughout North America. You are setting up a data storage system that will handle bank account transactions. You require ACID compliance and the ability to access data with SQL. Which solution is appropriate?

A. Store transaction in Cloud Spanner. Use locking read-write transactions.
B. Store transaction data in Cloud SQL. Use a federated query BigQuery for analysis.
C. Store transaction data in BigQuery. Disabled the query cache to ensure consistency.
D. Store transaction data in Cloud Spanner. Enable stale reads to reduce latency.

정답：A

설명：
As the documentation primarily says ACID compliance for Spanner, not Cloud SQL.
https://cloud.google.com/blog/topics/developers-practitioners/your-google-cloud-database-options-explained Also, spanner supports read-write transactions for use cases, as handling bank transactions:
https://cloud.google.com/spanner/docs/transactions#read-write_transactions

질문 # 258
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