ADP Musterprüfungsfragen - ADPZertifizierung & ADPTestfagen

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Google ADP Exam Syllabus Topics:

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

>> ADP Probesfragen <<

Google ADP: Associate Data Practitioner braindumps PDF & Testking echter Test

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q64-Q69):

64. 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. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

Antwort: C

65. Frage
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A. Cloud Storage CloudSQL for PostgreSQL Bigtable
B. Filestore Cloud SQL for PostgreSQL Datastore
C. Cloud Storage BigQuery Firestore
D. Filestore Bigtable BigQuery

Antwort: C

66. Frage
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
B. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
C. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
D. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Antwort: A

67. Frage
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Cloud Data Fusion pipelines.
B. Use Cloud Composer operators.
C. Use Dataform workflows.
D. Use Dataflow pipelines.

Antwort: C

68. Frage
Your company uses Looker to visualize and analyze sales dat
a. 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 a single Explore with all sales metrics. Build the dashboard using this Explore.
B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
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: A

69. Frage
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