Google Associate-Data-Practitioner模擬体験、Associate-Data-Practitioner日本語版

あなたのIT能力が権威的に認められるのがほしいですか。GoogleのAssociate-Data-Practitioner試験に合格するのは最良の方法の一です。我々Japancertの開発するGoogleのAssociate-Data-Practitionerソフトはあなたに一番速い速度でGoogleのAssociate-Data-Practitioner試験のコツを把握させることができます。豊富な資料、便利なページ構成と購入した一年間の無料更新はあなたにGoogleのAssociate-Data-Practitioner試験に合格させる最高の支持です。

Google Associate-Data-Practitioner 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
トピック 2	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
トピック 3	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

>> Google Associate-Data-Practitioner模擬体験 <<

Google Associate-Data-Practitioner日本語版 & Associate-Data-Practitionerトレーニング費用

Associate-Data-Practitioner試験に合格することが、最高のキャリアの機会です。関連する証明書の豊富な経験は、企業があなたの選択のために一連の専門的な空席を開くために重要です。状況によってはあなたを助けたり破ったりすることができるこの運命的な試験について、当社はこれらのAssociate-Data-Practitioner練習資料を説明責任を持って作成しました。他の場所に受け入れられる可能性が高くなり、より高い給料や受け入れが得られることを理解しています。

Google Cloud Associate Data Practitioner 認定 Associate-Data-Practitioner 試験問題 (Q37-Q42):

質問 # 37
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.
* Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
* Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
* Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.

質問 # 38
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

A. Create a data masking rule.
B. Grant the appropriate 1AM permissions on the dataset.
C. Add a policy tag in BigQuery.
D. Create a row-level access policy.

正解：D

解説：
Creating a row-level access policy in BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

質問 # 39
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
B. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
C. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let's assess:
* Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
* Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training.
Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
* Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).

質問 # 40
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.

Which Google Cloud services should you recommend for the pipeline?

A. 1. BigQuery
2. Analytics Hub
B. 1. Dataproc Serverless
2. Bigtable
C. 1. Dataflow
2. BigQuery
D. 1. Cloud Composer
2. Cloud SQL for MySQL

正解：C

解説：
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

質問 # 41
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

A. Retry messages until they are acknowledged.
B. Seek back to the last acknowledged message.
C. Implement flow control on the subscribers
D. Forward unacknowledged messages to a dead-letter topic.

正解：C

解説：
Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

質問 # 42
......

Japancertが提供したGoogleのAssociate-Data-Practitionerトレーニング資料を利用してから試験に合格することがとてもたやすことになって、これは今までがないことです。これは試験に合格した受験生の一人が言ったのです。Japancertが提供したGoogleのAssociate-Data-Practitionerトレーニング資料はあなたの雑然とした考えを整理できます。そうしたらあなたは心理的なストレスを減らせるだけでなく、気楽に試験に受かることもできます。Japancertには一部の問題と解答を無料に提供して差し上げますから、もし私の話を信じないのなら、試用版を使ってみてください。利用してみたら効果があるかどうか自分でよく知っているようになります。あなたに絶対向いていると信じていますよ。

Associate-Data-Practitioner日本語版: https://www.japancert.com/Associate-Data-Practitioner.html

Mike Fisher Mike Fisher

Biographie

Google Associate-Data-Practitioner模擬体験、Associate-Data-Practitioner日本語版

Google Associate-Data-Practitioner 認定試験の出題範囲：

Google Associate-Data-Practitioner日本語版 & Associate-Data-Practitionerトレーニング費用

Google Cloud Associate Data Practitioner 認定 Associate-Data-Practitioner 試験問題 (Q37-Q42):

Ressources

Support