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NEW QUESTION: 1
企業のWebサイトを使用して製品を一般に販売しています。サイトはApplication Load Balancer（ALB）の背後にあるAuto ScalingグループのAmazon EC2インスタンスで実行されています。AmazonCloudFrontディストリビューションもあり、AWS WAFがSQLインジェクション攻撃から保護するために使用されていますALBはCloudFrontディストリビューションの起源です。セキュリティログの最近のレビューにより、ウェブサイトへのアクセスをブロックする必要がある外部の悪意のあるIPが明らかになりました。ソリューションアーキテクトがアプリケーションを保護するために何をすべきですか？」
A. CloudFrontディストリビューションのネットワークACLを変更して、悪意のあるIPアドレスの拒否ルールを追加します
B. AWS WAFの構成を変更して、悪意のあるIPアドレスをブロックするIP一致条件を追加します
C. ALBの背後にあるターゲットグループのEC2インスタンスのセキュリティグループを変更して、悪意のあるIPアドレスを拒否します
D. ALBの背後にあるターゲットグループのEC2インスタンスのネットワークACLを変更して、悪意のあるIPアドレスを拒否します
Answer: B
Explanation:
A new version of the AWS Web Application Firewall was released in November 2019. With AWS WAF classic you create "IP match conditions", whereas with AWS WAF (new version) you create
"IP set match statements". Look out for wording on the exam.
The IP match condition / IP set match statement inspects the IP address of a web request's origin against a set of IP addresses and address ranges.
Use this to allow or block web requests based on the IP addresses that the requests originate from.
AWS WAF supports all IPv4 and IPv6 address ranges. An IP set can hold up to 10,000 IP addresses or IP address ranges to check.
CORRECT: "Modify the configuration of AWS WAF to add an IP match condition to block the malicious IP address" is the correct answer.
INCORRECT: "Modify the network ACL on the CloudFront distribution to add a deny rule for the malicious IP address" is incorrect as CloudFront does not sit within a subnet so network ACLs do not apply to it.
INCORRECT: "Modify the network ACL for the EC2 instances in the target groups behind the ALB to deny the malicious IP address" is incorrect as the source IP addresses of the data in the EC2 instances' subnets will be the ELB IP addresses.
INCORRECT: "Modify the security groups for the EC2 instances in the target groups behind the ALB to deny the malicious IP address." is incorrect as you cannot create deny rules with security groups.
References:
https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-type-ipset- match.html
NEW QUESTION: 2
会社には、15分ごとに更新される天気予報を生成するアプリケーションがあります。これは、サイズがそれぞれ約20バイト（予測ごとに20ギガバイト）の10億の一意の位置の出力解像度で更新されます。 1時間ごとに、世界中の予測データに約500万回（1秒あたり1,400リクエスト）アクセスされ、天気イベント中は最大10倍アクセスされます。予測データは更新ごとに上書きされます。現在の天気予報アプリケーションのユーザーは、クエリに対する応答がリクエストごとに2秒以内に返されることを期待しています。
必要な要求率と応答時間を満たす設計はどれですか？
A. Amazon EFSボリュームに予測場所を保存します。 Amazon EFSボリュームをマウントしたAmazon EC2インスタンスのAuto ScalingフリートのElastic Load BalancingグループをターゲットとするAmazon CloudFrontディストリビューションを作成します。 CloudFrontディストリビューションで、設定されたキャッシュ制御タイムアウトを15分間に設定します。
B. 予測場所をAmazon S3に個別のオブジェクトとして保存します。 EC2インスタンスのAuto ScalingフリートのElastic Load BalancingグループをターゲットとするAmazon CloudFrontディストリビューションを作成し、S3オブジェクトのオリジンをクエリします。 CloudFrontディストリビューションでキャッシュ制御タイムアウトを15分間に設定します。
C. 予測場所をAmazon ESクラスターに保存します。クエリに応答するAWS Lambda関数をオリジンとして、Amazon API GatewayエンドポイントをターゲットとするAmazon CloudFrontディストリビューションを使用します。
キャッシュ制御タイムアウトを15分間に設定して、API GatewayステージでAPIキャッシュを有効にします。
D. 予測場所をAmazon ESクラスターに保存します。クエリに応答するAWS Lambda関数をオリジンとして、API GatewayエンドポイントをターゲットとするAmazon CloudFrontディストリビューションを使用します。 15分間エッジの場所でデータをローカルにキャッシュするAmazon Lambda @ Edge関数を作成します。
Answer: C
NEW QUESTION: 3
Which is SLT Technical Details?
A. Object-based transformation capabilities allow business objects or business processes to be consistently transformed within Object-Based Conversion or across Object-Based Migration SAP systems with the option to cover all historical data or just data of the actual fiscal year at any point in time
B. Brownfield (AKA: Pick up best scenarios within company): Systems to be merged will not be in operative use afterwards. A new system with a new organizational structure, but existing processes, is be created. In case of a "selective" migration, access to source systems for historical information is required.
C. Greenfield (AKA: Start from scratch): Systems to be merged will not be in operative use afterwards. A new system with a new organizational structure and processes will be created. In case of a "selective" migration, access to source systems for historical information is required.
Answer: A
NEW QUESTION: 4
Which of the following biometrics methods provides the HIGHEST accuracy and is LEAST accepted by users?
A. Hand Geometry
B. Palm Scan
C. Retina scan
D. Fingerprint
Answer: C
Explanation:
Retina based biometric involves analyzing the layer of blood vessels situated at the back of the eye. An established technology, this technique involves using a low-intensity light source through an optical coupler to scan the unique patterns of the retina. Retinal scanning can be quite accurate but does require the user to look into a receptacle and focus on a given point. This is not particularly convenient if you wear glasses or are concerned about having close contact with the reading device. For these reasons, retinal scanning is not warmly accepted by all users, even though the technology itself can work well.
For your exam you should know the information below:
Biometrics Biometrics verifies an individual's identity by analyzing a unique personal attribute or behavior, which is one of the most effective and accurate methods of verifying identification and not well received by society. Biometrics is a very sophisticated technology; thus, it is much more expensive and complex than the other types of identity verification processes. A biometric system can make authentication decisions based on an individual's behavior, as in signature dynamics, but these can change over time and possibly be forged. Biometric systems that base authentication decisions on physical attributes (such as iris, retina, or fingerprint) provide more accuracy because physical attributes typically don't change, absent some disfiguring injury, and are harder to impersonate Biometrics is typically broken up into two different categories. The first is the physiological. These are traits that are physical attributes unique to a specific individual. Fingerprints are a common example of a physiological trait used in biometric systems. The second category of biometrics is known as behavioral. The behavioral authentication is also known as continuous authentication. The behavioral/continuous authentication prevents session hijacking attack. This is based on a characteristic of an individual to confirm his identity. An example is signature Dynamics.
Physiological is "what you are" and behavioral is "what you do." When a biometric system rejects an authorized individual, it is called a Type I error (false rejection rate). When the system accepts impostors who should be rejected, it is called a Type II error (false acceptance rate). The goal is to obtain low numbers for each type of error, but Type II errors are the most dangerous and thus the most important to avoid. When comparing different biometric systems, many different variables are used, but one of the most important metrics is the crossover error rate (CER). This rating is stated as a percentage and represents the point at which the false rejection rate equals the false acceptance rate. This rating is the most important measurement when determining the system's accuracy. A biometric system that delivers a CER of 3 will be more accurate than a system that delivers a CER of 4 Crossover error rate (CER) is also called equal error rate (EER). Throughput describes the process of authenticating to a biometric system. This is also referred to as the biometric system response time. The primary consideration that should be put into the purchasing and implementation of biometric access control are user acceptance, accuracy and processing speed.
Biometric Considerations In addition to the access control elements of a biometric system, there are several other considerations that are important to the integrity of the control environment. These are: Resistance to counterfeiting Data storage requirements User acceptance Reliability and Target User and approach
Fingerprint Fingerprints are made up of ridge endings and bifurcations exhibited by friction ridges and other detailed characteristics called minutiae. It is the distinctiveness of these minutiae that gives each individual a unique fingerprint. An individual places his finger on a device that reads the details of the fingerprint and compares this to a reference file. If the two match, the individual's identity has been verified.
Palm Scan The palm holds a wealth of information and has many aspects that are used to identify an individual. The palm has creases, ridges, and grooves throughout that are unique to a specific person. The palm scan also includes the fingerprints of each finger. An individual places his hand on the biometric device, which scans and captures this information. This information is compared to a reference file, and the identity is either verified or rejected.
Hand Geometry The shape of a person's hand (the shape, length, and width of the hand and fingers) defines hand geometry. This trait differs significantly between people and is used in some biometric systems to verify identity. A person places her hand on a device that has grooves for each finger. The system compares the geometry of each finger, and the hand as a whole, to the information in a reference file to verify that person's identity.
Retina Scan A system that reads a person's retina scans the blood-vessel pattern of the retina on the backside of the eyeball. This pattern has shown to be extremely unique between different people. A camera is used to project a beam inside the eye and capture the pattern and compare it to a reference file recorded previously.
Iris Scan An iris scan is a passive biometric control The iris is the colored portion of the eye that surrounds the pupil. The iris has unique patterns, rifts, colors, rings, coronas, and furrows. The uniqueness of each of these characteristics within the iris is captured by a camera and compared with the information gathered during the enrollment phase. When using an iris pattern biometric system, the optical unit must be positioned so the sun does not shine into the aperture; thus, when implemented, it must have proper placement within the facility.
Signature Dynamics When a person signs a signature, usually they do so in the same manner and speed each time. Signing a signature produces electrical signals that can be captured by a biometric system. The physical motions performed when someone is signing a document create these electrical signals. The signals provide unique characteristics that can be used to distinguish one individual from another. Signature dynamics provides more information than a static signature, so there are more variables to verify when confirming an individual's identity and more assurance that this person is who he claims to be.
Keystroke Dynamics Whereas signature dynamics is a method that captures the electrical signals when a person signs a name, keystroke dynamics captures electrical signals when a person types a certain phrase. As a person types a specified phrase, the biometric system captures the speed and motions of this action. Each individual has a certain style and speed, which translate into unique signals. This type of authentication is more effective than typing in a password, because a password is easily obtainable. It is much harder to repeat a person's typing style than it is to acquire a password.
Voice Print People's speech sounds and patterns have many subtle distinguishing differences. A biometric system that is programmed to capture a voice print and compare it to the information held in a reference file can differentiate one individual from another. During the enrollment process, an
individual is asked to say several different words.
Facial Scan
A system that scans a person's face takes many attributes and characteristics into account.
People have different bone structures, nose ridges, eye widths, forehead sizes, and chin shapes.
These are all captured during a facial scan and compared to an earlier captured scan held within a
reference record. If the information is a match, the person is positively identified.
Hand Topography
Whereas hand geometry looks at the size and width of an individual's hand and fingers, hand
topology looks at the different peaks and valleys of the hand, along with its overall shape and
curvature. When an individual wants to be authenticated, she places her hand on the system. Off
to one side of the system, a camera snaps a side-view picture of the hand from a different view
and angle than that of systems that target hand geometry, and thus captures different data. This
attribute is not unique enough to authenticate individuals by itself and is commonly used in
conjunction with hand geometry.
Vascular Scan
Valcular Scan uses the blood vessel under the first layer of skin.
The following answers are incorrect:
Fingerprint - Fingerprints are made up of ridge endings and bifurcations exhibited by friction ridges
and other detailed characteristics called minutiae. It is the distinctiveness of these minutiae that
gives each individual a unique fingerprint. An individual places his finger on a device that reads the
details of the fingerprint and compares this to a reference file. If the two match, the individual's
identity has been verified.
Hand Geometry - The shape of a person's hand (the shape, length, and width of the hand and
fingers) defines hand geometry. This trait differs significantly between people and is used in some
biometric systems to verify identity. A person places her hand on a device that has grooves for
each finger. The system compares the geometry of each finger, and the hand as a whole, to the
information in a reference file to verify that person's identity.
Palm Scan - The palm holds a wealth of information and has many aspects that are used to
identify an individual. The palm has creases, ridges, and grooves throughout that are unique to a
specific person. The palm scan also includes the fingerprints of each finger. An individual places
his hand on the biometric device, which scans and captures this information. This information is
compared to a reference file, and the identity is either verified or rejected.
Following reference(s) were/was used to create this question: CISA review manual 2014 Page number 330 and 331 Official ISC2 guide to CISSP CBK 3rd Edition Page number 924