ADP Prüfungsfragen Prüfungsvorbereitungen 2026: Associate Data Practitioner - Zertifizierungsprüfung Google ADP in Deutsch Englisch pdf downloaden

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing and Analytics	20-30%	- Apply statistical methods for analysis - Aggregate and summarize data - Use BigQuery and SQL for analytics - Query and analyze datasets - Build and maintain data pipelines
Data Visualization and Insights	20-30%	- Present data insights to stakeholders - Create dashboards and reports - Interpret and communicate findings - Build visualizations using Looker Studio - Choose appropriate visualization types
Data Preparation and Exploration	20-30%	- Transform and prepare data for analysis - Ingest and acquire data - Explore data through visualization and queries - Identify data quality issues - Perform exploratory data analysis (EDA)
Data-Driven Decision Making	10-20%	- Assess data quality and completeness - Translate business requirements into data solutions - Define success metrics - Identify stakeholders and requirements

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ADP PrüfungGuide, Google ADP Zertifikat - Associate Data Practitioner

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q58-Q63):

58. 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 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.
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 Cloud Run function that runs the pipeline daily. Monitor the functions execution using 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: A

59. Frage
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Antwort: A

60. Frage
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
B. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
C. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
D. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

Antwort: D

61. Frage
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
C. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Antwort: D

62. Frage
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
B. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
C. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
D. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.

Antwort: C

63. Frage
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