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GoogleのProfessional-Data-Engineer認定試験の最新な問題集

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Google Professional-Data-Engineer認定試験に備えるには、候補者は、Google Cloudプラットフォームとその関連サービスの知識と同様に、データエンジニアリングの基礎を確実に理解している必要があります。候補者は、Google Cloudプラットフォームの公式ドキュメント、オンラインコース、練習試験など、さまざまな研究リソースを使用して、試験に備えることができます。さらに、候補者は、データ処理システム、データウェアハウジング、およびデータ分析ツールの操作経験がある必要があります。

Google Professional-Data-Engineer認定試験は、かなりの量の準備が必要な厳格な試験です。候補者は、ビッグデータソリューションを扱う豊富な経験を持ち、データ処理と分析の最新の傾向に精通している必要があります。この認定は業界で高く評価されており、新しいキャリアの機会とより高い給与につながる可能性があります。

Google Professional-Data-Engineer 認定試験は、データエンジニアリング、データ分析、機械学習、ビッグデータ処理など、さまざまな分野で候補者の知識とスキルをテストするオンライン試験です。この試験は複数選択式の問題から構成され、Google Cloud Platform、そのサービス、およびその機能についての深い理解が必要です。候補者は、ビジネス要件を満たすデータ処理システムの設計と実装の熟練度を証明する必要があります。

>> Professional-Data-Engineer受験資格 <<

Professional-Data-Engineer認定資格試験、Professional-Data-Engineer日本語版

我々の目標はProfessional-Data-Engineer試験に準備するあなたに試験に合格させることです。この目標を実現するようには、我が社のMogiExamは試験改革のとともにめざましく推進していき、最も専門的なProfessional-Data-Engineer問題集をリリースしています。現時点で我々のGoogle　Professional-Data-Engineer問題集を使用しているあなたは試験にうまくパースできると信じられます。心配なく我々の真題を利用してください。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q272-Q277):

質問 # 272
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.
D. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

正解：C、E

質問 # 273
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data.
Which two actions should you take? (Choose two.)

A. Configure your Cloud Dataflow pipeline to use local execution
B. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
C. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
D. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
E. Increase the number of nodes in the Cloud Bigtable cluster

正解：B、C

解説：
A - Local execution is useful for testing and debugging purposes, especially if your pipeline can use smaller in-memory datasets.
B- https://cloud.google.com/dataflow/docs/guides/specifying-exec-params C- increases both read and write performance D- Flatten merges multiple PCollection objects into a single logical PCollection.
E- Consider using CoGroupByKey if you have multiple data sets that provide information about related things .

質問 # 274
You are using BigQuery's ML.GENERATE_TEXT function to write marketing materials for a new product launch. The problem is, the AI is generating random text that does not always relate to the product. You want the simplest way to improve the output to generate a marketing copy. What should you do?

A. Seed the prompt with examples of product descriptions for similar products.
B. Experiment with different values for the temperature setting in ML.GENERATE_TEXT.
C. Experiment with different values for the top_p setting in ML.GENERATE_TEXT.
D. Fine-tune a more specialized language model on a dataset of product features.

正解：B

解説：
The temperature parameter is the primary control for randomness and "creativity" in Large Language Models (LLMs) used within BigQuery ML.
* Randomness vs. Focus: A high temperature (e.g., closer to 1.0) leads to more random, diverse, and sometimes irrelevant output because the model is more likely to choose lower-probability tokens. If the AI is generating "random text," lowering the temperature (e.g., to 0.2 or 0.1) makes the model more deterministic and focused on the most likely next tokens related to the input prompt.
* Simplest Way: Adjusting a single parameter in a SQL function is the "simplest" approach compared to gathering datasets for few-shot prompting (A) or the high complexity and cost of fine-tuning (C).
* temperature vs. top_p: While top_p (nucleus sampling) also affects randomness, temperature is the standard first-line control for the overall "entropy" of the model's responses. Official Google documentation often suggests adjusting temperature first to curb hallucination or excessive randomness.
Reference: Google Cloud Documentation on ML.GENERATE_TEXT:
"temperature: A value in the range [0.0, 1.0]... It controls the degree of randomness in token selection.
Lower temperature values are good for prompts that require a more deterministic and less open-ended response, while higher temperature values can lead to more diverse or creative results." (Source: ML.
GENERATE_TEXT arguments)
"To get more predictable responses from the model, use a lower temperature." (Source: BigQuery ML generative AI overview)

質問 # 275
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

A. Use the LIMIT keyword to reduce the number of rows returned.
B. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
C. Create a separate table for each ID.
D. Recreate the table with a partitioning column and clustering column.

正解：D

解説：
https://cloud.google.com/bigquery/docs/best-practices-costs

質問 # 276
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. Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
B. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.
Store data in HDFS. Change references in scripts from hdfs:// to gs://
C. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://
D. Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

正解：D

質問 # 277
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