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Google Associate-Data-Practitioner Prüfungsplan:

Thema
Einzelheiten

Thema 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.

Thema 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

Thema 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.

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Associate-Data-Practitioner Studienmaterialien: Google Cloud Associate Data Practitioner - Associate-Data-Practitioner Torrent Prüfung & Associate-Data-Practitioner wirkliche Prüfung

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Google Cloud Associate Data Practitioner Associate-Data-Practitioner Prüfungsfragen mit Lösungen (Q46-Q51):

46. Frage
Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team's Python skills. What should you do?

A. Use Dataform with assertions.
B. Use Dataflow and pre-built templates.
C. Deploy Cloud Data Fusion and included plugins.
D. Use Cloud Composer with pre-built operators.

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation:
The tool must be fully managed, support batch ETL, integrate with multiple Google Cloud services, and leverage Python skills.
* Option A: Dataform is SQL-focused for ELT within BigQuery, not Python-centric, and lacks broad service integration for extraction.
* Option B: Cloud Data Fusion is a visual ETL tool, not Python-focused, and requires more UI-based configuration than coding.
* Option C: Cloud Composer (managed Apache Airflow) is fully managed, supports batch ETL via DAGs, integrates with various Google Cloud services (e.g., BigQuery, GCS) through operators, and allows custom Python code in tasks. It's ideal for Python developers per the "Cloud Composer" documentation.

47. Frage
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

A. 1. Online transactions: Bigtable
2. Customer feedback: Cloud Storage
3. Social media activity: CloudSQL for MySQL
B. 1. Online transactions: Cloud SQL for MySQL
2. Customer feedback: BigQuery
3. Social media activity: Cloud Storage
C. 1. Online transactions: BigQuery
2. Customer feedback: Cloud Storage
3. Social media activity: BigQuery
D. 1. Online transactions: Cloud Storage
2. Customer feedback: Cloud Storage
3. Social media activity: Cloud Storage

Antwort: C

Begründung:
Online transactions: Storing the transactional data in BigQuery is ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback: Storing customer feedback in Cloud Storage is appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity: Storing real-time social media activity in BigQuery is optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.

48. Frage
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
C. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Antwort: D

Begründung:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

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

Antwort: B

Begründung:
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.

50. Frage
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

A. Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub
/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
B. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
C. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
D. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

Antwort: A

Begründung:
UsingDataflowto implement a streaming pipeline triggered by anOBJECT_FINALIZEnotification from Pub
/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

51. Frage
......

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