GCP-DE科目対策、GCP-DE試験問題解説集

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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Storing and managing data | 20% | - Optimizing storage performance and cost
- 1. Configuring storage for access patterns
- 2. Implementing cost optimization strategies
- Implementing storage solutions
- 1. Using data storage services appropriately
- 2. Managing data lifecycle and retention
- 3. Designing data warehouses and data lakes
|
| Designing data processing systems | 24% | - Designing data pipelines
- 1. Planning for data migration and integration
- 2. Designing for data transformation and enrichment
- 3. Defining architecture for batch and streaming processing
- Planning data solutions
- 1. Selecting appropriate storage solutions
- 2. Designing for reliability, scalability, and efficiency
- 3. Planning for data security and compliance
|
| Ingesting and processing data | 25% | - Transforming data
- 1. Optimizing transformations for performance
- 2. Managing schema evolution
- 3. Applying data processing logic
- Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Handling data quality and consistency
- 3. Implementing ingestion mechanisms
|
| Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Supporting model training and serving
- 2. Feature engineering and data preparation
- Enabling data analysis
- 1. Optimizing query performance
- 2. Preparing datasets for querying and reporting
- 3. Implementing data sharing and governance
|
| Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
- Operationalizing workloads
- 1. Monitoring and troubleshooting pipelines
- 2. Orchestrating and scheduling jobs
- 3. Ensuring reliability and recoverability
|
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GCP-DE試験問題解説集、GCP-DE関連受験参考書
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Google Data Engineer 認定 GCP-DE 試験問題 (Q38-Q43):
質問 # 38
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- C. Create a cron schedule in Cloud Dataprep.
- D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
正解:D
質問 # 39
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?
- A. Use an SSD persistent disk and 50% preemptible worker
- B. Deploy a Cloud Dataproc cluste
- C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- D. Install the Cloud Storage connector, and store the data in Cloud Storag
- E. Change references in scripts from hdfs:// to gs://
- F. Change references in scripts from hdfs:// to gs://
- G. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
- H. Use a standard persistent disk and 50% preemptible worker
- I. Deploy a Cloud Dataproc cluste
- J. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
- K. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
正解:B
質問 # 40
When a Cloud Bigtable node fails, is lost.
- A. the time dimension
- B. all data
- C. no data
- D. the last transaction
正解:C
解説:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview
質問 # 41
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?
- A. Remove the features that have null values for more than 50% of the training records.
- B. Instead of feeding in each feature individually, average their values in batches of 3.
- C. Combine highly co-dependent features into one representative feature.
- D. Eliminate features that are highly correlated to the output labels.
正解:C
質問 # 42
Which of these is not a supported method of putting data into a partitioned table?
- A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- C. Create a partitioned table and stream new records to it every day.
- D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
正解:D
解説:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
質問 # 43
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