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Google ADP Exam Syllabus Topics:
Section
Weight
Objectives
Topic 1: Data Processing and Analytics
20-30%
- Aggregate and summarize data
- Apply statistical methods for analysis
- Use BigQuery and SQL for analytics
- Build and maintain data pipelines
- Query and analyze datasets
Topic 2: Data Preparation and Exploration
20-30%
- Ingest and acquire data
- Explore data through visualization and queries
- Perform exploratory data analysis (EDA)
- Identify data quality issues
- Transform and prepare data for analysis
Topic 3: Data-Driven Decision Making
10-20%
- Assess data quality and completeness
- Define success metrics
- Identify stakeholders and requirements
- Translate business requirements into data solutions
Topic 4: Data Visualization and Insights
20-30%
- Build visualizations using Looker Studio
- Create dashboards and reports
- Interpret and communicate findings
- Choose appropriate visualization types
- Present data insights to stakeholders
Google Associate Data Practitioner Sample Questions:
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability. What should you do?
A. Create a multi-region bucket, and upload the files to this bucket.
B. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
C. Create a dual-region bucket, and upload the files to this bucket.
D. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?
A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
B. Apply a filter to only show products with a positive profit margin.
C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
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. 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.
B. 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.
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.
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
A. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
B. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
C. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?
A. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
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