Professional-Data-Engineer試験の準備方法｜最新のProfessional-Data-Engineer模擬試験問題集試験｜100％合格率のGoogle Certified Professional Data Engineer Exam専門試験

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あなたの夢は何ですか。あなたのキャリアでいくつかの輝かしい業績を行うことを望まないのですか。きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。IT業種で仕事しているあなたは、夢を達成するためにどんな方法を利用するつもりですか。実際には、IT認定試験を受験して認証資格を取るのは一つの良い方法です。最近、GoogleのProfessional-Data-Engineer試験は非常に人気のある認定試験です。あなたもこの試験の認定資格を取得したいのですか。さて、はやく試験を申し込みましょう。PassTestはあなたを助けることができますから、心配する必要がないですよ。

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment
Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Designing data processing systems	22%	- Data architecture and storage design 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) 2. Designing scalable and cost-effective data models - Batch and streaming data processing design 1. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs
Ensuring solution quality	28%	- Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads - Security and governance 1. IAM and access control in GCP 2. Data governance and compliance

>> Professional-Data-Engineer模擬試験問題集 <<

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q330-Q335):

質問 # 330
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

正解：D

質問 # 331
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
B. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table.
C. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table.
D. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

正解：C

解説：
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them. You can then load the de-identified data into a BigQuery table for analysis. Reference:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

質問 # 332
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison. What should you do?

A. Create stratified random samples using the OVER() function and compare equivalent samples from each table.
B. Select random samples from the tables using the HASH() function and compare the samples.
C. Select random samples from the tables using the RAND() function and compare the samples.
D. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

正解：D

解説：
Full comparison with this option, rest are comparison on sample which doesn't ensure all the data will be ok.

質問 # 333
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

A. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
C. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
D. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

正解：B

解説：
Avro is compressed format and dataflow for parallel pipeline and bigquery for storage.

質問 # 334
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data has invalid rows that were skipped on import.
B. The CSV data has not gone through an ETL phase before loading into BigQuery.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data loaded in BigQuery is not flagged as CSV.

正解：C

質問 # 335
......

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Professional-Data-Engineer試験の準備方法｜最新のProfessional-Data-Engineer模擬試験問題集試験｜100％合格率のGoogle Certified Professional Data Engineer Exam専門試験

Google Professional-Data-Engineer Exam Syllabus Topics:

最新-正確的なProfessional-Data-Engineer模擬試験問題集試験-試験の準備方法Professional-Data-Engineer専門試験

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q330-Q335):