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Google ADP Exam Syllabus Topics:

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

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ADP New Exam Materials | High-quality ADP: Associate Data Practitioner 100% Pass

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Google Associate Data Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Coldline storage.
B. Store the data in Cloud Storage using Nearline storage.
C. Store the data in Cloud Storage using Standard storage.
D. Store the data in Cloud Storage using Archive storage.

Answer: B

NEW QUESTION # 46
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 dual-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 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.
D. Create a multi-region bucket, and upload the files to this bucket.

Answer: A

NEW QUESTION # 47
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
C. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
D. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

Answer: B

NEW QUESTION # 48
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. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
D. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

Answer: B

NEW QUESTION # 49
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
C. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
D. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.

Answer: A

NEW QUESTION # 50
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