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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Use Cloud Tasks to schedule and run the jobs asynchronously.
B. Use Cloud Scheduler to schedule the jobs to run.
C. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Answer: D

NEW QUESTION # 61
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A. Cloud Storage BigQuery Firestore
B. Cloud Storage CloudSQL for PostgreSQL Bigtable
C. Filestore Cloud SQL for PostgreSQL Datastore
D. Filestore Bigtable BigQuery

Answer: A

NEW QUESTION # 62
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;

Answer: C

NEW QUESTION # 63
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. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B. Migrate the Spark jobs to Dataproc Serverless.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

Answer: B

NEW QUESTION # 64
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A. Use Cloud Composer to orchestrate and run data workflows.
B. Use Cloud Data Fusion to visually design and manage the pipelines.
C. Use Dataform to build, orchestrate, and monitor the pipelines.
D. Use Dataproc to run MapReduce jobs for distributed data processing.

Answer: C

NEW QUESTION # 65
......

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