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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

Answer: C

NEW QUESTION # 43
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. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins 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. 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.

Answer: B

NEW QUESTION # 44
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. Cloud Composer Cloud SQL for MySQL
B. BigQuery Analytics Hub
C. Dataflow BigQuery
D. Dataproc Serverless Bigtable

Answer: C

NEW QUESTION # 45
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 a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib 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. 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.

Answer: C

NEW QUESTION # 46
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A. Use the gcloud storage command to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C. Use Cloud Client Libraries to transfer the data over the internet.
D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Answer: D

NEW QUESTION # 47
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