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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q41-Q46):

NEW QUESTION # 41
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B. Create BigQuery scheduled queries to define the transformations in SQL.
C. Use Dataform to define the transformations in SQLX.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: C

NEW QUESTION # 42
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
B. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
D. Apply a filter to only show products with a positive profit margin.

Answer: A

NEW QUESTION # 43
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 Nearline storage.
B. Store the data in Cloud Storage using Standard storage.
C. Store the data in Cloud Storage using Archive storage.
D. Store the data in Cloud Storage using Coldline storage.

Answer: A

NEW QUESTION # 44
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Update dataset privileges by using the SQL GRANT statement.
B. Encrypt the data by using customer-managed encryption keys (CMEK).
C. Enable access control by using IAM roles.
D. Export the data to Cloud Storage, and use signed URLs to authorize access.

Answer: C

NEW QUESTION # 45
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 Scheduler to schedule the jobs to run.
B. Use Cloud Tasks to schedule and run the jobs asynchronously.
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 # 46
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