ADP Actual Collection: Associate Data Practitioner - ADP Quiz Braindumps & ADP Exam Guide

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Exploration	20-30%	- Ingest and acquire data - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis - Identify data quality issues - Explore data through visualization and queries
Topic 2: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Query and analyze datasets - Aggregate and summarize data - Apply statistical methods for analysis - Use BigQuery and SQL for analytics
Topic 3: Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Interpret and communicate findings - Present data insights to stakeholders - Build visualizations using Looker Studio - Create dashboards and reports
Topic 4: 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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Google Associate Data Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
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. 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.
B. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
C. Grant the data analyst the BigQuery Job User 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.

Answer: D

NEW QUESTION # 37
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. Filestore Bigtable BigQuery
B. Cloud Storage CloudSQL for PostgreSQL Bigtable
C. Cloud Storage BigQuery Firestore
D. Filestore Cloud SQL for PostgreSQL Datastore

Answer: C

NEW QUESTION # 38
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. 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.
B. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
C. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
D. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer: B

NEW QUESTION # 39
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. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

NEW QUESTION # 40
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. 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.
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. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
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.

Answer: C

NEW QUESTION # 41
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