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Google ADP Exam Syllabus Topics:

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

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Newest Associate Data Practitioner Valid Questions - ADP Updated Torrent & ADP Reliable Training

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Google Associate Data Practitioner Sample Questions (Q12-Q17):

NEW QUESTION # 12
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
B. Use Cloud Composer to orchestrate the Spark job and email the report.
C. Use Cloud Run functions to trigger the Spark job and email the report.
D. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

Answer: A

NEW QUESTION # 13
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 Data Fusion to visually design and manage the pipelines.
B. Use Dataform to build, orchestrate, and monitor the pipelines.
C. Use Cloud Composer to orchestrate and run data workflows.
D. Use Dataproc to run MapReduce jobs for distributed data processing.

Answer: B

NEW QUESTION # 14
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. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
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. Use Cloud Scheduler to schedule the jobs to run.

Answer: B

NEW QUESTION # 15
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
B. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
C. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
D. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

Answer: B

NEW QUESTION # 16
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
C. Create a Cloud Run function that runs daily and deletes files older than seven days.
D. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Answer: D

NEW QUESTION # 17
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