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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q42-Q47):

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

Answer: A

NEW QUESTION # 43
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
B. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Answer: B

NEW QUESTION # 44
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

A. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
B. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
C. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
D. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.

Answer: A

NEW QUESTION # 45
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 Compute Engine.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

NEW QUESTION # 46
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
B. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

Answer: B

NEW QUESTION # 47
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