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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance
Topic 2: Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Topic 3: Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
Topic 4: Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Developing batch and streaming workflows 2. Implementing ingestion mechanisms 3. Handling data quality and consistency
Topic 5: Designing data processing systems	24%	- Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 3. Selecting appropriate storage solutions - Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing

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Google Data Engineer Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A. A non-sequential numeric ID
B. A stock symbol followed by a timestamp
C. A sequential numeric ID
D. A timestamp followed by a stock symbol

Answer: C,D

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 50
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A. BigQuery and Cloud Bigtable
B. Cloud Datastore and Cloud Bigtable
C. BigQuery and Cloud Storage
D. Cloud Bigtable and Cloud SQL

Answer: A

NEW QUESTION # 51
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
C. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Answer: D

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 52
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. cron
B. Cloud Composer
C. Workflow Templates on Cloud Dataproc
D. Cloud Scheduler

Answer: C

NEW QUESTION # 53
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
C. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
D. Re-create the tables using DD
E. Write an Apache Beam pipeline that creates a BigQuery table per data
F. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: A

NEW QUESTION # 54
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