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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Aggregate and summarize data - Query and analyze datasets
Topic 3: 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 4: Data Visualization and Insights	20-30%	- Create dashboards and reports - Present data insights to stakeholders - Interpret and communicate findings - Build visualizations using Looker Studio - Choose appropriate visualization types

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Google Associate Data Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
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 the gcloud storage command to replicate the data across the buckets in both regions.
C. 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.
D. Create a multi-region bucket, and upload the files to this bucket.

Answer: A

NEW QUESTION # 66
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. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: C

NEW QUESTION # 67
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Compare the confusion matrix.
B. Evaluate data drift.
C. Compare the two different models.
D. Evaluate the data skewness.

Answer: B

NEW QUESTION # 68
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
B. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C. Migrate the Spark jobs to Dataproc Serverless.
D. Migrate the Spark jobs to Dataproc on Compute Engine.

Answer: C

NEW QUESTION # 69
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
D. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Answer: D

NEW QUESTION # 70
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