Professional-Data-Engineer的中率 & Professional-Data-Engineer予想試験

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

>> Professional-Data-Engineer的中率 <<

ユニークなProfessional-Data-Engineer的中率 & 合格スムーズProfessional-Data-Engineer予想試験 | 信頼できるProfessional-Data-Engineer受験対策解説集

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q426-Q431):

質問 # 426
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

正解：B

解説：
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.
Reference:
https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options


質問 # 427
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

正解：A

解説：
https://towardsdatascience.com/understanding-hyperparameters-and-its-optimisation-techniques-f0debba07568


質問 # 428
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

正解：D

解説：
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms


質問 # 429
Your financial services company has a critical daily reconciliation process that involves several distinct steps: fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?

正解：B

解説：
Defining each step as a separate task in a Cloud Composer DAG provides fine-grained dependency management, built-in observability, and clear task-level monitoring. This approach allows individual steps to be retried or re-run independently, supports robust failure handling and alerting for SLA breaches such as missing the 7:00 AM deadline, and aligns with best practices for orchestrating complex, dependent data pipelines.


質問 # 430
You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring.
You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?

正解：B

解説：
To troubleshoot query performance issues related to job queuing or slot contention in BigQuery, using administrative resource charts along with querying the INFORMATION_SCHEMA is the best approach.
Here's why option D is the best choice:
Administrative Resource Charts:
BigQuery provides detailed resource charts that show slot usage and job performance over time. These charts help identify patterns of slot contention and peak usage times.
INFORMATION_SCHEMA Queries:
The INFORMATION_SCHEMA tables in BigQuery provide detailed metadata about query jobs, including execution times, slots consumed, and other performance metrics.
Running queries on INFORMATION_SCHEMA allows you to pinpoint specific jobs causing contention and analyze their performance characteristics.
Comprehensive Analysis:
Combining administrative resource charts with detailed queries on INFORMATION_SCHEMA provides a holistic view of the system's performance.
This approach enables you to identify and address the root causes of performance issues, whether they are due to slot contention, inefficient queries, or other factors.
Steps to Implement:
Access Administrative Resource Charts:
Use the Google Cloud Console to view BigQuery's administrative resource charts. These charts provide insights into slot utilization and job performance metrics over time.
Run INFORMATION_SCHEMA Queries:
Execute queries on BigQuery's INFORMATION_SCHEMA to gather detailed information about job performance. For example:
SELECT
creation_time,
job_id,
user_email,
query,
total_slot_ms / 1000 AS slot_seconds,
total_bytes_processed / (1024 * 1024 * 1024) AS processed_gb,
total_bytes_billed / (1024 * 1024 * 1024) AS billed_gb
FROM
`region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)
AND state = 'DONE'
ORDER BY
slot_seconds DESC
LIMIT 100;
Analyze and Optimize:
Use the information gathered to identify bottlenecks, optimize queries, and adjust resource allocations as needed to improve performance.
Reference Links:
Monitoring BigQuery Slots
BigQuery INFORMATION_SCHEMA
BigQuery Performance Best Practices


質問 # 431
......

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