Professional-Data-Engineer認定試験トレーリング & Professional-Data-Engineer合格内容

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当社ShikenPASSは、常にProfessional-Data-Engineer認定の傾向を追ってきました。当社の研究開発チームは、Professional-Data-Engineer試験で出題される質問を調査するだけではありません。 Professional-Data-Engineer練習資料の内容は、試験のすべての質問が含まれるように慎重に選択されています。そして、私たちの教材には、いつでも、どこでも、読む、Google Certified Professional Data Engineer Examテストする、勉強するのに役立つ3つの形式があります。つまり、当社の製品を使用すると、試験の準備を効率的に行うことができます。 Professional-Data-Engineer認定を希望される場合、当社Googleの製品が最適です。

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring - Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation
Topic 2: Maintaining and automating data workloads	18%	- Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Topic 3: Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Transforming and cleaning data 2. Orchestrating data workflows 3. Ingesting data from various sources
Topic 4: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Topic 5: Designing data processing systems	20%	- Designing for business requirements 1. Designing for scalability and elasticity 2. Selecting appropriate storage solutions 3. Designing for reliability and fault tolerance - Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection

>> Professional-Data-Engineer認定試験トレーリング <<

Professional-Data-Engineer合格内容、Professional-Data-Engineer模擬モード

当社の唯一の目的は、各顧客が試験に合格するのを支援するとともに、短時間で重要な認定を取得することです。試験に合格し、自分にとって非常に重要なProfessional-Data-Engineer認定を取得したい場合は、当社のProfessional-Data-Engineer認定準備資料を選択して、試験の理解を深めることを強くお勧めします。あなたが準備するつもりです。弊社からProfessional-Data-Engineer試験教材を購入することに決めた場合、試験に合格し、他の人よりもリラックスした方法で認定資格を取得できると考えています。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q149-Q154):

質問 # 149
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
B. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
C. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
D. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
E. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

正解：B

質問 # 150
You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model.
You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

A. When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery"s ML. EVALUATE clause without specifying any transformations on the raw input data.
B. Preprocess all data using Dataflow. At prediction time, use BigOuery"s ML. EVALUATE clause without specifying any further transformations on the input data.
C. Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.
D. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML.
EVALUATE

正解：A

解説：
https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning

質問 # 151
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A. Cloud SQL for PostgreSQL
B. Cloud Datastore
C. Cloud Bigtable
D. BigQuery

正解：D

質問 # 152
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

A. Dataproc Runner
B. Dataproc Editor
C. Dataproc Worker
D. Dataproc Viewer

正解：C

解説：
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

質問 # 153
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

A. Increase the regularization parameters
B. Reduce the number of training examples
C. Use a smaller set of features
D. Get more training examples
E. Use a larger set of features
F. Decrease the regularization parameters

正解：A、C、D

質問 # 154
......

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