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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q48-Q53):

NEW QUESTION # 48
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
B. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
C. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

Answer: C

NEW QUESTION # 49
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C. Use Cloud Scheduler to schedule the jobs to run.
D. Use Cloud Tasks to schedule and run the jobs asynchronously.

Answer: B

NEW QUESTION # 50
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. Create BigQuery scheduled queries to define the transformations in SQL.
B. Use Dataform to define the transformations in SQLX.
C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
D. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

Answer: B

NEW QUESTION # 51
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. Grant each partner read access to the BigQuery dataset by using 1AM roles.
B. 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.
C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D. 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.

Answer: C

NEW QUESTION # 52
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
B. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Answer: B

NEW QUESTION # 53
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