Associate Data Practitioner Updated Torrent - ADP Study Questions & ADP Updated Material

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q35-Q40):

NEW QUESTION # 35
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 # 36
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. Migrate the Spark jobs to Dataproc Serverless.
D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Answer: C

NEW QUESTION # 37
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Answer: B

NEW QUESTION # 38
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
B. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
C. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
D. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

Answer: D

NEW QUESTION # 39
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. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Create a Cloud Run function that runs daily and deletes files older than seven days.

Answer: B

NEW QUESTION # 40
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