Maximize Your Success with PDFDumps Customizable ADP Associate Data Practitioner Practice Test

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q31-Q36):

NEW QUESTION # 31
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
B. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
C. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Answer: B

NEW QUESTION # 32
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 # 33
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

Answer: C

NEW QUESTION # 34
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. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Answer: A

NEW QUESTION # 35
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set a bucket retention policy.
B. Change the bucket storage class to Archive.
C. Set temporary object holds.
D. Enable Object Versioning.

Answer: A

NEW QUESTION # 36
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