ADP Online Prüfung & ADP Zertifizierungsantworten

Nach der Schulzeit haben wir mehr Verantwortungen und die Zeit fürs Lernen vermindert sich. Wenn Sie sich im IT-Bereich besser entwickeln möchten, dann ist die internationale Zertifizierungsprüfung wie Google ADP Prüfung zu bestehen sehr notwendig. Wir ITZert bieten Sie mit alle Kräfte vieler IT-Profis die effektivste Hilfe bei der Google ADP Prüfung. 3 Versionen (PDF, online sowie Software) von Google ADP Prüfungsunterlagen haben Ihre besondere Überlegenheit. Dadurch, dass Sie die kostenlose Demos probieren, können Sie nach Ihre Gewohnheiten die geeignete Version wählen.

Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Insights	20-30%	- Create dashboards and reports - Present data insights to stakeholders - Choose appropriate visualization types - Interpret and communicate findings - Build visualizations using Looker Studio
Topic 2: Data-Driven Decision Making	10-20%	- Define success metrics - Translate business requirements into data solutions - Identify stakeholders and requirements - Assess data quality and completeness
Topic 3: Data Preparation and Exploration	20-30%	- Transform and prepare data for analysis - Perform exploratory data analysis (EDA) - Identify data quality issues - Ingest and acquire data - Explore data through visualization and queries
Topic 4: Data Processing and Analytics	20-30%	- Use BigQuery and SQL for analytics - Apply statistical methods for analysis - Build and maintain data pipelines - Aggregate and summarize data - Query and analyze datasets

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q51-Q56):

51. Frage
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

Antwort: C

52. 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. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
B. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
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

53. Frage
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
B. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
C. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
D. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

Antwort: A

54. Frage
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
B. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Antwort: D

55. Frage
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Cloud Storage FUSE.
B. Use Storage Transfer Service.
C. Use a Transfer Appliance.
D. Use gcloud storage commands.

Antwort: B

56. Frage
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