Valid Associate Data Practitioner braindumps pdf & ADP valid dumps

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q47-Q52):

NEW QUESTION # 47
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 Dataform to define the transformations in SQLX.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Answer: A

NEW QUESTION # 48
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

Answer: D

NEW QUESTION # 49
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
B. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
D. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

Answer: B

NEW QUESTION # 50
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. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Migrate the Spark jobs to Dataproc Serverless.
D. Migrate the Spark jobs to Dataproc on Compute Engine.

Answer: C

NEW QUESTION # 51
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
C. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Answer: A

NEW QUESTION # 52
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