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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 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.
Thema 3	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

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Associate-Data-Practitioner Exam, Associate-Data-Practitioner Prüfungsfragen

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Google Cloud Associate Data Practitioner Associate-Data-Practitioner Prüfungsfragen mit Lösungen (Q87-Q92):

87. Frage
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?

A. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
B. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
C. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
D. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.

Antwort: C

Begründung:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.

88. 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 an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
B. 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.
C. 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.
D. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

Antwort: A

Begründung:
Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification 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.

89. Frage
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
C. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
D. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.

Antwort: C

Begründung:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow's web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.

90. Frage
You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

A. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
B. Write and execute a Python script using the BigQuery Storage Write API library.
C. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
D. Execute the bq load command on your local machine.

Antwort: D

Begründung:
Comprehensive and Detailed In-Depth Explanation:
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
* Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging-more effort than necessary for a one-time task.
* Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting-far too complex for a simple load.
* Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.
table file.csv). It's pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.

91. Frage
Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Antwort: B

Begründung:
Creating asingle Explorewith all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker's recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
* Option A: Materialized views in BigQuery optimize queries but bypass Looker's modeling layer, reducing flexibility.
* Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering
/drill-down.
* Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.

92. Frage
......

Wenn Sie Ihre Position in der konkurrenzfähigen Gesellschaft durch die Google Associate-Data-Practitioner Zertifizierungsprüfung festigen und Ihre fachliche Fähigkeiten verbessern wollen, müssen Sie gute Fachkenntnisse besitzen und sich viel Mühe für die Prüfung geben. Aber es ist nicht so einfach, die Google Associate-Data-Practitioner Zertifizierungsprüfung zu bestehen. Vielleicht durch die Google Associate-Data-Practitioner Zertifizierungsprüfung können Sie Ihnen der IT-Branche vorstellen. Aber man braucht nicht unbedingt viel Zeit und Energie, die Fachkenntnisse kennenzulernen. Sie können die Schulungsunterlagen zur Google Associate-Data-Practitioner Zertifizierungsprüfung von It-Pruefung wählen. Sie werden zielgerichtet nach den IT-Zertifizierungsprüfungen entwickelt. Mit ihr können Sie mühelos die schwierige Google Associate-Data-Practitioner Zertifizierungsprüfung bestehen.

Associate-Data-Practitioner Exam: https://www.it-pruefung.com/Associate-Data-Practitioner.html

Dan Stone Dan Stone

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Associate-Data-Practitioner Mit Hilfe von uns können Sie bedeutendes Zertifikat der Associate-Data-Practitioner einfach erhalten!

Google Associate-Data-Practitioner Prüfungsplan:

Associate-Data-Practitioner Exam, Associate-Data-Practitioner Prüfungsfragen

Google Cloud Associate Data Practitioner Associate-Data-Practitioner Prüfungsfragen mit Lösungen (Q87-Q92):

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