ADP Torrent Anleitung - ADP Studienführer & ADP wirkliche Prüfung

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Google ADP Exam Syllabus Topics:

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

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Google ADP Übungsmaterialien - ADP Zertifizierungsprüfung

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
D. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

Antwort: C

13. Frage
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Cloud Data Fusion to visually design and manage the pipeline.
B. Use Dataproc to run the transformations on a cluster.
C. Use Dataform to define the transformations in SQLX.
D. Use Dataflow with a custom template for the transformation logic.

Antwort: B

14. Frage
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
B. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

Antwort: C

15. Frage
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
C. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Antwort: D

16. Frage
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Antwort: A

17. Frage
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