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Associate-Data-Practitioner考試心得 & Associate-Data-Practitioner參考資料

擁有Google Associate-Data-Practitioner認證考試證書可以幫助在IT領域找工作的人獲得更好的就業機會，也將會為成功的IT事業做好鋪墊。

Google Associate-Data-Practitioner 考試大綱：

主題
簡介

主題 1

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

主題 2

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.

主題 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.

>> Associate-Data-Practitioner考試心得 <<

最新的Google Associate-Data-Practitioner考古題

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最新的 Google Cloud Platform Associate-Data-Practitioner 免費考試真題 (Q66-Q71):

問題 #66
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
B. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
C. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

答案：C

解題說明：
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.

問題 #67
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Dataform workflows.
B. Use Cloud Composer operators.
C. Use Cloud Data Fusion pipelines.
D. Use Dataflow pipelines.

答案：A

解題說明：
Dataform workflows are the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

問題 #68
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

A. Use Google-managed encryption keys (GMEK).
B. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
C. Use customer-supplied encryption keys (CSEK).
D. Use customer-managed encryption keys (CMEK).

答案：A

解題說明：
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.

問題 #69
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

A. Compare the confusion matrix.
B. Evaluate the data skewness.
C. Compare the two different models.
D. Evaluate data drift.

答案：D

解題說明：
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.

問題 #70
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. Create a row-level access policy.
C. Grant the appropriate 1AM permissions on the dataset.
D. Add a policy tag in BigQuery.

答案：B

解題說明：
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.

問題 #71
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手上能拿到一些實用的認證證書，無疑為自己的就業開拓了一番新的領土和創造了一些機會。Associate-Data-Practitioner 是全球最大的網絡設備公司 Google 公司的認可的初級技術認證，在整個 Google 認證體系中處于售前規劃方向的基礎證書，有了Associate-Data-Practitioner 認證你的平均年薪將不低于10萬人民幣。雖然獲取 Associate-Data-Practitioner 認證需要投入額外的時間與金錢，但事實證明IT認證的投入產出是值得的，對於未來的職業發展非常有利。

Associate-Data-Practitioner參考資料: https://tw.fast2test.com/Associate-Data-Practitioner-premium-file.html

Jack Ward Jack Ward

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