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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
B. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
C. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
D. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

Answer: A

NEW QUESTION # 54
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 on Compute Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

NEW QUESTION # 55
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
B. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
C. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
D. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Answer: D

NEW QUESTION # 56
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Answer: A

NEW QUESTION # 57
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use gcloud storage commands.
B. Use a Transfer Appliance.
C. Use Cloud Storage FUSE.
D. Use Storage Transfer Service.

Answer: D

NEW QUESTION # 58
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