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Google ADP Exam Syllabus Topics:

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

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

NEW QUESTION # 41
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Answer: A

NEW QUESTION # 42
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. 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.
B. Use Cloud Scheduler to schedule the jobs to run.
C. Use Cloud Tasks to schedule and run the jobs asynchronously.
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 # 43
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
C. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.

Answer: A

NEW QUESTION # 44
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
C. Use Cloud Run functions to transform and load the data into BigQuery.
D. Use Dataflow to transform the data and write the transformed data to BigQuery.

Answer: D

NEW QUESTION # 45
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Use authorized views to share query results with the payroll specialist.
B. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
C. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
D. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

Answer: A

NEW QUESTION # 46
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